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@@ -0,0 +1,9536 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "BfnvA9dThcTd",
+ "metadata": {
+ "id": "BfnvA9dThcTd"
+ },
+ "source": [
+ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
+ "\n",
+ "
![](\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\")
\n",
+ "
![](\"https://github.com/unslothai/unsloth/raw/main/images/Discord)
\n",
+ "
![](\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\")
Join Discord if you need help + ⭐
Star us on Github ⭐\n",
+ "
\n",
+ " \n",
+ "
\n",
+ " [150/150 02:52, Epoch 0/1]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 5 | \n",
+ " 2.080000 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 1.519800 | \n",
+ "
\n",
+ " \n",
+ " | 15 | \n",
+ " 1.281600 | \n",
+ "
\n",
+ " \n",
+ " | 20 | \n",
+ " 1.119500 | \n",
+ "
\n",
+ " \n",
+ " | 25 | \n",
+ " 0.807800 | \n",
+ "
\n",
+ " \n",
+ " | 30 | \n",
+ " 0.947200 | \n",
+ "
\n",
+ " \n",
+ " | 35 | \n",
+ " 0.996400 | \n",
+ "
\n",
+ " \n",
+ " | 40 | \n",
+ " 1.039300 | \n",
+ "
\n",
+ " \n",
+ " | 45 | \n",
+ " 0.830200 | \n",
+ "
\n",
+ " \n",
+ " | 50 | \n",
+ " 0.812000 | \n",
+ "
\n",
+ " \n",
+ " | 55 | \n",
+ " 1.016400 | \n",
+ "
\n",
+ " \n",
+ " | 60 | \n",
+ " 0.746500 | \n",
+ "
\n",
+ " \n",
+ " | 65 | \n",
+ " 0.895400 | \n",
+ "
\n",
+ " \n",
+ " | 70 | \n",
+ " 0.950500 | \n",
+ "
\n",
+ " \n",
+ " | 75 | \n",
+ " 0.842000 | \n",
+ "
\n",
+ " \n",
+ " | 80 | \n",
+ " 0.982500 | \n",
+ "
\n",
+ " \n",
+ " | 85 | \n",
+ " 0.781900 | \n",
+ "
\n",
+ " \n",
+ " | 90 | \n",
+ " 0.716800 | \n",
+ "
\n",
+ " \n",
+ " | 95 | \n",
+ " 0.952200 | \n",
+ "
\n",
+ " \n",
+ " | 100 | \n",
+ " 0.805700 | \n",
+ "
\n",
+ " \n",
+ " | 105 | \n",
+ " 0.695200 | \n",
+ "
\n",
+ " \n",
+ " | 110 | \n",
+ " 0.729800 | \n",
+ "
\n",
+ " \n",
+ " | 115 | \n",
+ " 0.789200 | \n",
+ "
\n",
+ " \n",
+ " | 120 | \n",
+ " 0.846300 | \n",
+ "
\n",
+ " \n",
+ " | 125 | \n",
+ " 0.698600 | \n",
+ "
\n",
+ " \n",
+ " | 130 | \n",
+ " 0.600000 | \n",
+ "
\n",
+ " \n",
+ " | 135 | \n",
+ " 0.720300 | \n",
+ "
\n",
+ " \n",
+ " | 140 | \n",
+ " 0.746200 | \n",
+ "
\n",
+ " \n",
+ " | 145 | \n",
+ " 0.640300 | \n",
+ "
\n",
+ " \n",
+ " | 150 | \n",
+ " 0.743600 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=150, training_loss=0.9111059761047363, metrics={'train_runtime': 190.2769, 'train_samples_per_second': 0.788, 'train_steps_per_second': 0.788, 'total_flos': 2806047248793600.0, 'train_loss': 0.9111059761047363})"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "_S8Ny-4GqEZ2",
+ "metadata": {
+ "id": "_S8Ny-4GqEZ2"
+ },
+ "source": [
+ "Let's see if the fine tuning worked by generating a sample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "Mmlgke5lghFi",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Mmlgke5lghFi",
+ "outputId": "0e7c895e-3588-45b7-8cbe-dca4e902a9aa"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<|im_start|>system\n",
+ "You are given a problem with an image.\n",
+ " Think about the problem and provide your working out.\n",
+ " Place it between and .\n",
+ " Then, provide your solution between <|im_end|>\n",
+ "<|im_start|>user\n",
+ "<|vision_start|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|vision_end|>What number of purple metallic balls are there?<|im_end|>\n",
+ "<|im_start|>assistant\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of purple metallic balls in the described scene. Let me go through each object one by one to check if they meet both criteria: being purple and metallic, and being a ball.\n",
+ "\n",
+ "First object: A large blue metal cube. The color here is blue, not purple. So this doesn't count. Next.\n",
+ "\n",
+ "Second object: A small brown rubber cube. Brown and rubber, so no. Not a ball either. So that's another no.\n",
+ "\n",
+ "Third object: A small green metal cylinder. Green, metal, but cylinder shape. No ball. So that's also a no.\n",
+ "\n",
+ "Fourth object: A large blue rubber sphere. Blue, rubber, and a sphere. So this is a blue rubber sphere. The color is blue, not purple. Also, it's a sphere, not a ball. So this one doesn't qualify either.\n",
+ "\n",
+ "So after checking all four objects, none of them are purple or metallic, and none of them are balls. Therefore, the answer should be zero.\n",
+ " 0 <|im_end|>\n"
+ ]
+ }
+ ],
+ "source": [
+ "sample = dataset[0]\n",
+ "\n",
+ "message = [\n",
+ " {\"role\":\"system\",\n",
+ " \"content\": f\"\"\"You are given a problem with an image.\n",
+ " Think about the problem and provide your working out.\n",
+ " Place it between {reasoning_start} and {reasoning_end}.\n",
+ " Then, provide your solution between {solution_start}{solution_end}\"\"\"\n",
+ " },\n",
+ " {\"role\": \"user\",\n",
+ " \"content\": [\n",
+ " {\"type\": \"image\"},\n",
+ " {\"type\":\"text\",\"text\":f\"{sample['problem']}\"},\n",
+ " ]}]\n",
+ "\n",
+ "image =sample['image']\n",
+ "\n",
+ "input_text = tokenizer.apply_chat_template(message, add_generation_prompt=True)\n",
+ "inputs = tokenizer(\n",
+ " image,\n",
+ " input_text,\n",
+ " add_special_tokens=False,\n",
+ " return_tensors=\"pt\",\n",
+ ").to(\"cuda\")\n",
+ "\n",
+ "\n",
+ "from transformers import TextStreamer\n",
+ "_ = model.generate(\n",
+ " **inputs,\n",
+ " temperature = 0.1,\n",
+ " max_new_tokens = 1024,\n",
+ " streamer = TextStreamer(tokenizer, skip_prompt = False),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "_6SYwz8uqMiY",
+ "metadata": {
+ "id": "_6SYwz8uqMiY"
+ },
+ "source": [
+ "Looks like it works fine!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59R2aMiVqVs_",
+ "metadata": {
+ "id": "59R2aMiVqVs_"
+ },
+ "source": [
+ "We'll define regex patterns to extract both the expected output format and the model's response, which will be used for reward attribution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "FbakXbKcax-Y",
+ "metadata": {
+ "id": "FbakXbKcax-Y"
+ },
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "\n",
+ "# Add optional EOS token matching\n",
+ "solution_end_regex = f\"{re.escape(solution_end)}[\\\\s]*\" + \\\n",
+ " f\"(?:{re.escape(tokenizer.eos_token)})?\"\n",
+ "\n",
+ "match_format = re.compile(\n",
+ " rf\"{re.escape(reasoning_end)}.*?\" \\\n",
+ " rf\"{re.escape(solution_start)}(.+?){solution_end_regex}\" \\\n",
+ " r\"[\\s]*$\",\n",
+ " flags=re.MULTILINE | re.DOTALL\n",
+ ")\n",
+ "solution = re.compile(r\"\\s*(.*?)\\s*\", flags=re.DOTALL)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "QP9AQl6bzZBV",
+ "metadata": {
+ "id": "QP9AQl6bzZBV"
+ },
+ "source": [
+ "Let's test the regex patterns we've defined.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "i6Wgset0zbXe",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "i6Wgset0zbXe",
+ "outputId": "32d51db4-0b9b-4167-df72-815bcd5da66b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Extracted answer: 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = \"blabla alz grlfrfffze 0 \"\n",
+ "\n",
+ "match = solution.search(response)\n",
+ "if match:\n",
+ " print(\"Extracted answer:\", match.group(1))\n",
+ "else:\n",
+ " print(\"No match found.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "F3kmHOO-K0bx",
+ "metadata": {
+ "id": "F3kmHOO-K0bx"
+ },
+ "source": [
+ "Looks like it works fine!\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ULqyOHdfdDh5",
+ "metadata": {
+ "id": "ULqyOHdfdDh5"
+ },
+ "source": [
+ "# Reward functions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "TAOPjr6WLj5D",
+ "metadata": {
+ "id": "TAOPjr6WLj5D"
+ },
+ "source": [
+ "We won’t go into much detail on the reward part here. For further information, please refer to the [Qwen3_(4B)-GRPO](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb#scrollTo=9HJxrS76h3Ds) notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "x3LXqsVTcX8h",
+ "metadata": {
+ "id": "x3LXqsVTcX8h"
+ },
+ "outputs": [],
+ "source": [
+ "def check_answer_exactly(prompts, completions, answer, **kwargs):\n",
+ " question = prompts[0][-1][\"content\"]\n",
+ "\n",
+ " responses = [completion[0][\"content\"] for completion in completions]\n",
+ " extracted_responses = [\n",
+ " guess.group(1)\n",
+ " if (guess := solution.search(r)) is not None else None\n",
+ " for r in responses\n",
+ " ]\n",
+ " scores = []\n",
+ " print(f\"predicted:{extracted_responses[0]}\")\n",
+ " print(f\"groundtruth:{answer[0]}\")\n",
+ " for guess, true_answer in zip(extracted_responses, answer):\n",
+ " true_answer = true_answer[0]\n",
+ " true_answer = solution.search(true_answer).group(1)\n",
+ " score = 0\n",
+ " if guess is None:\n",
+ " scores.append(-2.0)\n",
+ " continue\n",
+ "\n",
+ " if guess == true_answer:\n",
+ " score += 5.0\n",
+ " elif guess.strip() == true_answer.strip():\n",
+ " score += 3.5\n",
+ "\n",
+ " else:\n",
+ " score -= 4\n",
+ " scores.append(score)\n",
+ " return scores\n",
+ "\n",
+ "\n",
+ "\n",
+ "def match_format_approximately(completions, **kwargs):\n",
+ " scores = []\n",
+ " print(completions[0][0][\"content\"])\n",
+ " for completion in completions:\n",
+ " score = 0\n",
+ " response = completion[0][\"content\"]\n",
+ "\n",
+ " score += 0.5 if response.count(reasoning_start) == 1 else -1.0\n",
+ " score += 0.5 if response.count(reasoning_end) == 1 else -1.0\n",
+ " score += 0.5 if response.count(solution_start) == 1 else -1.0\n",
+ " score += 0.5 if response.count(solution_end) == 1 else -1.0\n",
+ " scores.append(score)\n",
+ " return scores\n",
+ "\n",
+ "def match_format_exactly(completions, **kwargs):\n",
+ " scores = []\n",
+ " for completion in completions:\n",
+ " score = 0\n",
+ " response = completion[0][\"content\"]\n",
+ " # Match if format is seen exactly!\n",
+ " if match_format.search(response) is not None: score += 2.0\n",
+ " scores.append(score)\n",
+ " return scores\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "il0x6HpkeJBR",
+ "metadata": {
+ "id": "il0x6HpkeJBR"
+ },
+ "source": [
+ "# Format the dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "XZpNY6GYqgQq",
+ "metadata": {
+ "id": "XZpNY6GYqgQq"
+ },
+ "source": [
+ "Here, I’m using a class to format the dataset on the fly during training. This approach is especially useful for image datasets, since offline formatting can be time-consuming and memory-intensive. Additionally, it lets you apply image transformations dynamically.\n",
+ "Alternatively, you can format the dataset offline using a simple lambda function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "Nynu_4TheNVz",
+ "metadata": {
+ "id": "Nynu_4TheNVz"
+ },
+ "outputs": [],
+ "source": [
+ "def format_sample_GRPO(sample):\n",
+ " content = [\n",
+ " {\"type\":\"image\"},\n",
+ " {\"type\":\"text\",\"text\":f\"{sample['problem']}\"}\n",
+ " ]\n",
+ "\n",
+ " return {\n",
+ " \"prompt\": [\n",
+ " {\"role\":\"system\",\n",
+ " \"content\":f\"\"\"You are given a problem.\n",
+ " Think about the problem and provide your working out.\n",
+ " Place it between {reasoning_start} and {reasoning_end}.\n",
+ " Then, provide your solution between {solution_start}{solution_end}\"\"\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": content\n",
+ " },],\n",
+ " \"image\": [sample['image']],\n",
+ " \"answer\": [sample['solution']]\n",
+ " }\n",
+ "\n",
+ "from torch.utils.data import Dataset\n",
+ "class LazyFormattedDataset(Dataset): #This class will format the dataset on the fly during training\n",
+ " def __init__(self, dataset, format_fn):\n",
+ " super().__init__()\n",
+ " self.dataset = dataset\n",
+ " self.format_fn = format_fn\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " item = self.dataset[idx]\n",
+ " return self.format_fn(item)\n",
+ "\n",
+ " def __len__(self):\n",
+ " return len(self.dataset)\n",
+ "\n",
+ "dataset_grpo=LazyFormattedDataset(dataset=dataset,format_fn=format_sample_GRPO) #The final dataset\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "zGKqtEDbdBe0",
+ "metadata": {
+ "id": "zGKqtEDbdBe0"
+ },
+ "source": [
+ "# GRPO Config"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fTCTD-RjLZVs",
+ "metadata": {
+ "id": "fTCTD-RjLZVs"
+ },
+ "source": [
+ "Here is the interesting part, you just have to set ``use_vision = True`` in GRPOConfig to enable VLM GRPO.\n",
+ "\n",
+ "Note that I lowered max_completion_length in order to reduce the reasoning trace and lower the training time.\n",
+ "\n",
+ "We use TensorBoard to monitor training progress. You can launch TensorBoard in another notebook, connect to the same environment, and run the following commands: \\\\\n",
+ "`%load_ext tensorboard` \\\\\n",
+ "`%tensorboard --logdir logs `"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "Fc4vfm9AdAtw",
+ "metadata": {
+ "id": "Fc4vfm9AdAtw"
+ },
+ "outputs": [],
+ "source": [
+ "from unsloth import is_bf16_supported\n",
+ "from trl import GRPOConfig,GRPOTrainer\n",
+ "\n",
+ "logging_dir=\"./logs\"\n",
+ "run = \"run_V1\"\n",
+ "log_path=f\"{logging_dir}/{run}\"\n",
+ "\n",
+ "FastVisionModel.for_training(model)\n",
+ "training_args = GRPOConfig(\n",
+ " use_vision = True, # Enable VLM GRPO\n",
+ " learning_rate = 5e-6,\n",
+ " adam_beta1 = 0.9,\n",
+ " adam_beta2 = 0.99,\n",
+ " beta=0.005, # KL regularization, decrease to encourage model exploration\n",
+ " temperature=1,\n",
+ " weight_decay = 0.1,\n",
+ " warmup_ratio = 0.1,\n",
+ " lr_scheduler_type = \"cosine\",\n",
+ " optim = \"adamw_8bit\",\n",
+ " bf16 = is_bf16_supported(),\n",
+ " fp16 = not is_bf16_supported(),\n",
+ " num_iterations = 1,\n",
+ " per_device_train_batch_size = 4,\n",
+ " gradient_accumulation_steps = 4, # Increase to 4 for smoother training\n",
+ " num_generations = 4, # Decrease if out of memory\n",
+ " max_prompt_length = 1024,\n",
+ " max_completion_length = 256,\n",
+ " #num_train_epochs = 1, # Set to 1 for a full training run\n",
+ " max_steps = 250,\n",
+ " save_steps = 50,\n",
+ " max_grad_norm = 0.1,\n",
+ " output_dir=\"./results\",\n",
+ " logging_dir=log_path,\n",
+ " logging_strategy=\"steps\",\n",
+ " logging_steps=1,\n",
+ " report_to=\"tensorboard\", #can use W&B\n",
+ ")\n",
+ "\n",
+ "trainer = GRPOTrainer(\n",
+ " model = model,\n",
+ " processing_class=tokenizer,\n",
+ " reward_funcs = [\n",
+ " match_format_exactly,\n",
+ " match_format_approximately,\n",
+ " check_answer_exactly,\n",
+ " ],\n",
+ " args = training_args,\n",
+ " train_dataset = dataset_grpo,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "sbGv_2vpfq5r",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "sbGv_2vpfq5r",
+ "outputId": "7ac8720a-4a21-49f2-aaa0-406da843e7e8"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
+ " \\\\ /| Num examples = 37,773 | Num Epochs = 1 | Total steps = 250\n",
+ "O^O/ \\_/ \\ Batch size per device = 4 | Gradient accumulation steps = 4\n",
+ "\\ / Data Parallel GPUs = 1 | Total batch size (4 x 4 x 1) = 16\n",
+ " \"-____-\" Trainable parameters = 82,169,856/3,000,000,000 (2.74% trained)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Okay, let's see. The question is asking how many blue objects have the same shape as the red rubber sphere. First, I need to identify all the objects in the scene that are blue and then check if their shape matches the red rubber sphere.\n",
+ "\n",
+ "Looking at the image, there are several objects listed. Let me go through each one:\n",
+ "\n",
+ "1. A large gray metal cube: This is a cube, not a sphere. So this doesn't count.\n",
+ "2. A small purple rubber cylinder: Cylinder, so not a sphere. So no.\n",
+ "3. A large cyan rubber cylinder: Cylinder again, so no.\n",
+ "4. A small brown rubber sphere: Sphere, but it's brown, not red. So this is different.\n",
+ "5. A large yellow metal sphere: Sphere, but color is yellow, not red. So no.\n",
+ "6. A small gray metal cube: Cube, so not a sphere. Not relevant.\n",
+ "7. A small purple rubber cylinder: Cylinder, not a sphere. So no.\n",
+ "8. A large purple metal cylinder: Cylinder, not a sphere. So no.\n",
+ "9. A small gray rubber cylinder: Cylinder, not a sphere. So no.\n",
+ "10. A large red rubber sphere: This is the red rubber sphere mentioned\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n"
+ ]
+ },
+ {
+ "data": {
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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+ " -3.187500 | \n",
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\n",
+ " \n",
+ " | 67 | \n",
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\n",
+ " \n",
+ " | 68 | \n",
+ " 0.009000 | \n",
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\n",
+ " \n",
+ " | 69 | \n",
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+ " -0.843750 | \n",
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+ " -1.187500 | \n",
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+ "
\n",
+ " \n",
+ " | 70 | \n",
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+ " -1.093750 | \n",
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+ " -1.500000 | \n",
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+ "
\n",
+ " \n",
+ " | 71 | \n",
+ " 0.003200 | \n",
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\n",
+ " \n",
+ " | 72 | \n",
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+ " -3.093750 | \n",
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+ " -1.093750 | \n",
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+ " -2.625000 | \n",
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\n",
+ " \n",
+ " | 73 | \n",
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+ " -0.375000 | \n",
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\n",
+ " \n",
+ " | 74 | \n",
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+ " 0.500000 | \n",
+ " 183.250000 | \n",
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+ " -0.250000 | \n",
+ " 2.323790 | \n",
+ " -0.750000 | \n",
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\n",
+ " \n",
+ " | 75 | \n",
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+ " 1.452369 | \n",
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+ " -2.187500 | \n",
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\n",
+ " \n",
+ " | 76 | \n",
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+ " 0.031250 | \n",
+ " 2.171549 | \n",
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\n",
+ " \n",
+ " | 77 | \n",
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\n",
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+ " | 78 | \n",
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\n",
+ " \n",
+ " | 79 | \n",
+ " 0.011800 | \n",
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+ " -1.937500 | \n",
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\n",
+ " \n",
+ " | 80 | \n",
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\n",
+ " \n",
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+ " 1.914582 | \n",
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\n",
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+ " | 82 | \n",
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\n",
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\n",
+ " \n",
+ " | 84 | \n",
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\n",
+ " \n",
+ " | 85 | \n",
+ " -0.006100 | \n",
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\n",
+ " \n",
+ " | 86 | \n",
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+ " -1.750000 | \n",
+ " 4.024922 | \n",
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\n",
+ " \n",
+ " | 87 | \n",
+ " 0.004000 | \n",
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+ " 2.012461 | \n",
+ " -1.250000 | \n",
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\n",
+ " \n",
+ " | 88 | \n",
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+ " -0.812500 | \n",
+ " 2.250000 | \n",
+ " -2.187500 | \n",
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\n",
+ " \n",
+ " | 89 | \n",
+ " 0.027800 | \n",
+ " -0.656250 | \n",
+ " 1.312500 | \n",
+ " 209.187500 | \n",
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+ " 0.312500 | \n",
+ " 187.909103 | \n",
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+ " 0.957427 | \n",
+ " 0.781250 | \n",
+ " 1.991388 | \n",
+ " -2.812500 | \n",
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+ "
\n",
+ " \n",
+ " | 90 | \n",
+ " 0.019300 | \n",
+ " -0.281250 | \n",
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+ " 0.031250 | \n",
+ " 2.171549 | \n",
+ " -1.312500 | \n",
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\n",
+ " \n",
+ " | 91 | \n",
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+ " 2.012461 | \n",
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\n",
+ " \n",
+ " | 92 | \n",
+ " 0.021200 | \n",
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+ " 218.000000 | \n",
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+ " 0.500000 | \n",
+ " 0.894427 | \n",
+ " -1.375000 | \n",
+ " 2.012461 | \n",
+ " -1.375000 | \n",
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\n",
+ " \n",
+ " | 93 | \n",
+ " 0.005400 | \n",
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+ " 2.256241 | \n",
+ " -3.000000 | \n",
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\n",
+ " \n",
+ " | 94 | \n",
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+ " -2.187500 | \n",
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\n",
+ " \n",
+ " | 95 | \n",
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+ " -0.562500 | \n",
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\n",
+ " \n",
+ " | 96 | \n",
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\n",
+ " \n",
+ " | 97 | \n",
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\n",
+ " \n",
+ " | 98 | \n",
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\n",
+ " \n",
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\n",
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\n",
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\n",
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\n",
+ " \n",
+ " | 103 | \n",
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\n",
+ " \n",
+ " | 104 | \n",
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\n",
+ " \n",
+ " | 105 | \n",
+ " 0.003500 | \n",
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+ " 2.012461 | \n",
+ " -1.250000 | \n",
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\n",
+ " \n",
+ " | 106 | \n",
+ " -0.002700 | \n",
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+ " 1.991388 | \n",
+ " -1.937500 | \n",
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\n",
+ " \n",
+ " | 107 | \n",
+ " 0.004500 | \n",
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+ " 2.424038 | \n",
+ " 177.750000 | \n",
+ " 138.000000 | \n",
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\n",
+ " \n",
+ " | 108 | \n",
+ " 0.008900 | \n",
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+ " -0.812500 | \n",
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\n",
+ " \n",
+ " | 109 | \n",
+ " 0.008600 | \n",
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\n",
+ " \n",
+ " | 110 | \n",
+ " 0.007100 | \n",
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+ " 0.786564 | \n",
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+ " 0.957427 | \n",
+ " 0.593750 | \n",
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+ " -0.562500 | \n",
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\n",
+ " \n",
+ " | 111 | \n",
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+ " -2.156250 | \n",
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+ " 0.687500 | \n",
+ " 182.800003 | \n",
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+ " -0.718750 | \n",
+ " 2.136732 | \n",
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\n",
+ " \n",
+ " | 112 | \n",
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\n",
+ " \n",
+ " | 113 | \n",
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\n",
+ " \n",
+ " | 114 | \n",
+ " 0.010500 | \n",
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\n",
+ " \n",
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\n",
+ " \n",
+ " | 116 | \n",
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\n",
+ " \n",
+ " | 117 | \n",
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\n",
+ " \n",
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\n",
+ " \n",
+ " | 119 | \n",
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\n",
+ " \n",
+ " | 120 | \n",
+ " 0.012300 | \n",
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\n",
+ " \n",
+ " | 121 | \n",
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\n",
+ " \n",
+ " | 122 | \n",
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\n",
+ " \n",
+ " | 123 | \n",
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\n",
+ " \n",
+ " | 124 | \n",
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\n",
+ " \n",
+ " | 125 | \n",
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\n",
+ " \n",
+ " | 126 | \n",
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\n",
+ " \n",
+ " | 127 | \n",
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\n",
+ " \n",
+ " | 128 | \n",
+ " -0.013800 | \n",
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\n",
+ " \n",
+ " | 129 | \n",
+ " 0.003900 | \n",
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\n",
+ " \n",
+ " | 130 | \n",
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\n",
+ " \n",
+ " | 131 | \n",
+ " 0.013500 | \n",
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\n",
+ " \n",
+ " | 132 | \n",
+ " -0.007200 | \n",
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\n",
+ " \n",
+ " | 133 | \n",
+ " 0.003800 | \n",
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\n",
+ " \n",
+ " | 134 | \n",
+ " -0.012400 | \n",
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\n",
+ " \n",
+ " | 135 | \n",
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\n",
+ " \n",
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\n",
+ " \n",
+ " | 137 | \n",
+ " 0.016900 | \n",
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+ "
\n",
+ " \n",
+ " | 138 | \n",
+ " 0.054300 | \n",
+ " 0.843750 | \n",
+ " 3.296298 | \n",
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+ " 0.894427 | \n",
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+ " 1.640313 | \n",
+ " -1.812500 | \n",
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\n",
+ " \n",
+ " | 139 | \n",
+ " 0.020800 | \n",
+ " 0.281250 | \n",
+ " 0.562500 | \n",
+ " 195.625000 | \n",
+ " 122.000000 | \n",
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+ " 148.666672 | \n",
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+ " 0.747128 | \n",
+ " 1.125000 | \n",
+ " 1.024695 | \n",
+ " 0.031250 | \n",
+ " 2.305564 | \n",
+ " -0.875000 | \n",
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+ "
\n",
+ " \n",
+ " | 140 | \n",
+ " -0.027500 | \n",
+ " 1.875000 | \n",
+ " 1.829368 | \n",
+ " 210.750000 | \n",
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+ " 0.375000 | \n",
+ " 183.600006 | \n",
+ " 115.000000 | \n",
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+ " 0.659860 | \n",
+ " 1.250000 | \n",
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+ " 2.121320 | \n",
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+ " 3.981206 | \n",
+ "
\n",
+ " \n",
+ " | 141 | \n",
+ " 0.037500 | \n",
+ " 3.187500 | \n",
+ " 4.144241 | \n",
+ " 177.562500 | \n",
+ " 117.000000 | \n",
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+ " 117.000000 | \n",
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+ " 0.831040 | \n",
+ " 1.875000 | \n",
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+ " 0.750000 | \n",
+ " -0.500000 | \n",
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+ "
\n",
+ " \n",
+ " | 142 | \n",
+ " 0.011000 | \n",
+ " 0.562500 | \n",
+ " 1.125000 | \n",
+ " 219.125000 | \n",
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+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -1.812500 | \n",
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+ "
\n",
+ " \n",
+ " | 143 | \n",
+ " 0.004800 | \n",
+ " 2.250000 | \n",
+ " 2.250000 | \n",
+ " 178.250000 | \n",
+ " 119.000000 | \n",
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+ " 2.000000 | \n",
+ " 0.000000 | \n",
+ " -1.750000 | \n",
+ " 4.024922 | \n",
+ "
\n",
+ " \n",
+ " | 144 | \n",
+ " 0.021900 | \n",
+ " -0.656250 | \n",
+ " 3.446406 | \n",
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+ " 202.000000 | \n",
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+ " 0.687500 | \n",
+ " 217.600006 | \n",
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+ " 0.957427 | \n",
+ " -0.906250 | \n",
+ " 2.154211 | \n",
+ " -0.375000 | \n",
+ " 3.242941 | \n",
+ "
\n",
+ " \n",
+ " | 145 | \n",
+ " 0.064600 | \n",
+ " -0.093750 | \n",
+ " 3.837019 | \n",
+ " 204.812500 | \n",
+ " 116.000000 | \n",
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+ " 0.437500 | \n",
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+ " 0.603338 | \n",
+ " 1.125000 | \n",
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+ " 0.218750 | \n",
+ " 2.205816 | \n",
+ " -1.437500 | \n",
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+ "
\n",
+ " \n",
+ " | 146 | \n",
+ " 0.040100 | \n",
+ " -1.125000 | \n",
+ " 2.598076 | \n",
+ " 241.812500 | \n",
+ " 212.000000 | \n",
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+ " -0.250000 | \n",
+ " 2.323790 | \n",
+ " -1.875000 | \n",
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+ "
\n",
+ " \n",
+ " | 147 | \n",
+ " 0.032200 | \n",
+ " 0.093750 | \n",
+ " 2.971661 | \n",
+ " 213.437500 | \n",
+ " 132.000000 | \n",
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+ " 0.375000 | \n",
+ " 187.900009 | \n",
+ " 132.000000 | \n",
+ " 249.000000 | \n",
+ " 0.626463 | \n",
+ " 1.250000 | \n",
+ " 1.000000 | \n",
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+ " 2.154211 | \n",
+ " -1.562500 | \n",
+ " 3.385632 | \n",
+ "
\n",
+ " \n",
+ " | 148 | \n",
+ " 0.025100 | \n",
+ " -1.687500 | \n",
+ " 1.978094 | \n",
+ " 231.187500 | \n",
+ " 170.000000 | \n",
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+ " 0.500000 | \n",
+ " 206.375000 | \n",
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+ " -0.250000 | \n",
+ " 2.323790 | \n",
+ " -2.437500 | \n",
+ " 2.220173 | \n",
+ "
\n",
+ " \n",
+ " | 149 | \n",
+ " 0.000300 | \n",
+ " -2.906250 | \n",
+ " 1.200586 | \n",
+ " 254.750000 | \n",
+ " 243.000000 | \n",
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+ " 0.812500 | \n",
+ " 249.333344 | \n",
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+ " 0.806226 | \n",
+ " -1.468750 | \n",
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+ " -1.812500 | \n",
+ " 1.939717 | \n",
+ "
\n",
+ " \n",
+ " | 150 | \n",
+ " 0.004700 | \n",
+ " -1.406250 | \n",
+ " 1.687500 | \n",
+ " 214.812500 | \n",
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+ " 0.031250 | \n",
+ " 2.305564 | \n",
+ " -2.562500 | \n",
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+ "
\n",
+ " \n",
+ " | 151 | \n",
+ " 0.014600 | \n",
+ " 0.281250 | \n",
+ " 2.511057 | \n",
+ " 200.750000 | \n",
+ " 129.000000 | \n",
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+ " 1.625000 | \n",
+ " 0.806226 | \n",
+ " 1.156250 | \n",
+ " 1.814008 | \n",
+ " -2.500000 | \n",
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+ "
\n",
+ " \n",
+ " | 152 | \n",
+ " 0.020400 | \n",
+ " -1.125000 | \n",
+ " 1.590990 | \n",
+ " 186.937500 | \n",
+ " 115.000000 | \n",
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+ " 2.250000 | \n",
+ " -2.687500 | \n",
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+ "
\n",
+ " \n",
+ " | 153 | \n",
+ " 0.010600 | \n",
+ " 2.250000 | \n",
+ " 1.774519 | \n",
+ " 223.125000 | \n",
+ " 173.000000 | \n",
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+ " 203.400009 | \n",
+ " 173.000000 | \n",
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+ " 1.250000 | \n",
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+ " 0.312500 | \n",
+ " 2.250000 | \n",
+ " 0.687500 | \n",
+ " 3.995310 | \n",
+ "
\n",
+ " \n",
+ " | 154 | \n",
+ " 0.046900 | \n",
+ " 1.687500 | \n",
+ " 2.424038 | \n",
+ " 206.937500 | \n",
+ " 151.000000 | \n",
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+ " 0.250000 | \n",
+ " 190.583344 | \n",
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+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -0.687500 | \n",
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\n",
+ " \n",
+ " | 155 | \n",
+ " 0.010200 | \n",
+ " 7.593750 | \n",
+ " 1.687500 | \n",
+ " 212.625000 | \n",
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+ " 1.718750 | \n",
+ " 1.125000 | \n",
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+ "
\n",
+ " \n",
+ " | 156 | \n",
+ " 0.016600 | \n",
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+ " 4.015028 | \n",
+ " 201.937500 | \n",
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+ " 1.437500 | \n",
+ " 1.537043 | \n",
+ " -0.375000 | \n",
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+ "
\n",
+ " \n",
+ " | 157 | \n",
+ " 0.003200 | \n",
+ " 1.125000 | \n",
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+ " 147.000000 | \n",
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+ " 0.894427 | \n",
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+ " 2.012461 | \n",
+ " -1.250000 | \n",
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\n",
+ " \n",
+ " | 158 | \n",
+ " 0.029300 | \n",
+ " -1.968750 | \n",
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+ " 2.305564 | \n",
+ " -3.125000 | \n",
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\n",
+ " \n",
+ " | 159 | \n",
+ " 0.015300 | \n",
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+ " 2.986538 | \n",
+ " 213.187500 | \n",
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+ " -1.125000 | \n",
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\n",
+ " \n",
+ " | 160 | \n",
+ " 0.010200 | \n",
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+ " 3.937500 | \n",
+ " 213.625000 | \n",
+ " 166.000000 | \n",
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\n",
+ " \n",
+ " | 161 | \n",
+ " 0.043400 | \n",
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+ " 1.948557 | \n",
+ " 189.250000 | \n",
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+ " -0.375000 | \n",
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+ "
\n",
+ " \n",
+ " | 162 | \n",
+ " 0.011900 | \n",
+ " -1.687500 | \n",
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\n",
+ " \n",
+ " | 163 | \n",
+ " 0.004400 | \n",
+ " -1.875000 | \n",
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+ " -3.125000 | \n",
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\n",
+ " \n",
+ " | 164 | \n",
+ " 0.007000 | \n",
+ " -0.843750 | \n",
+ " 1.687500 | \n",
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+ " 123.000000 | \n",
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+ " 2.305564 | \n",
+ " -1.187500 | \n",
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+ "
\n",
+ " \n",
+ " | 165 | \n",
+ " 0.013300 | \n",
+ " 2.625000 | \n",
+ " 1.500000 | \n",
+ " 211.687500 | \n",
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+ " 0.687500 | \n",
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+ "
\n",
+ " \n",
+ " | 166 | \n",
+ " 0.014200 | \n",
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+ " 1.687500 | \n",
+ " 212.437500 | \n",
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\n",
+ " \n",
+ " | 167 | \n",
+ " -0.015100 | \n",
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\n",
+ " \n",
+ " | 168 | \n",
+ " 0.027800 | \n",
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\n",
+ " \n",
+ " | 169 | \n",
+ " 0.002900 | \n",
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\n",
+ " \n",
+ " | 170 | \n",
+ " 0.003800 | \n",
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\n",
+ " \n",
+ " | 171 | \n",
+ " 0.003900 | \n",
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\n",
+ " \n",
+ " | 172 | \n",
+ " 0.018700 | \n",
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+ " 245.250000 | \n",
+ " 183.000000 | \n",
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+ " 0.812500 | \n",
+ " 198.666672 | \n",
+ " 183.000000 | \n",
+ " 208.000000 | \n",
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+ " -1.468750 | \n",
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+ " -0.687500 | \n",
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+ "
\n",
+ " \n",
+ " | 173 | \n",
+ " 0.009700 | \n",
+ " -0.562500 | \n",
+ " 1.948557 | \n",
+ " 210.187500 | \n",
+ " 137.000000 | \n",
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+ " 182.699997 | \n",
+ " 137.000000 | \n",
+ " 254.000000 | \n",
+ " 0.660174 | \n",
+ " 1.250000 | \n",
+ " 1.000000 | \n",
+ " 0.312500 | \n",
+ " 2.250000 | \n",
+ " -2.125000 | \n",
+ " 2.941088 | \n",
+ "
\n",
+ " \n",
+ " | 174 | \n",
+ " 0.022600 | \n",
+ " 0.093750 | \n",
+ " 2.986538 | \n",
+ " 220.500000 | \n",
+ " 132.000000 | \n",
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+ " 0.375000 | \n",
+ " 199.199997 | \n",
+ " 132.000000 | \n",
+ " 256.000000 | \n",
+ " 0.678875 | \n",
+ " 1.250000 | \n",
+ " 1.000000 | \n",
+ " 0.406250 | \n",
+ " 2.154211 | \n",
+ " -1.562500 | \n",
+ " 3.385632 | \n",
+ "
\n",
+ " \n",
+ " | 175 | \n",
+ " 0.048100 | \n",
+ " 5.343750 | \n",
+ " 2.812500 | \n",
+ " 175.125000 | \n",
+ " 116.000000 | \n",
+ " 256.000000 | \n",
+ " 0.062500 | \n",
+ " 169.733337 | \n",
+ " 116.000000 | \n",
+ " 225.000000 | \n",
+ " 0.848271 | \n",
+ " 1.875000 | \n",
+ " 0.500000 | \n",
+ " 1.718750 | \n",
+ " 1.125000 | \n",
+ " 1.750000 | \n",
+ " 4.358899 | \n",
+ "
\n",
+ " \n",
+ " | 176 | \n",
+ " 0.002300 | \n",
+ " 2.156250 | \n",
+ " 4.489151 | \n",
+ " 239.437500 | \n",
+ " 191.000000 | \n",
+ " 256.000000 | \n",
+ " 0.437500 | \n",
+ " 226.555557 | \n",
+ " 191.000000 | \n",
+ " 255.000000 | \n",
+ " 0.542025 | \n",
+ " 1.125000 | \n",
+ " 1.024695 | \n",
+ " 0.218750 | \n",
+ " 2.205816 | \n",
+ " 0.812500 | \n",
+ " 3.868139 | \n",
+ "
\n",
+ " \n",
+ " | 177 | \n",
+ " 0.002300 | \n",
+ " -0.656250 | \n",
+ " 1.312500 | \n",
+ " 227.125000 | \n",
+ " 152.000000 | \n",
+ " 256.000000 | \n",
+ " 0.562500 | \n",
+ " 190.000015 | \n",
+ " 152.000000 | \n",
+ " 220.000000 | \n",
+ " 0.484230 | \n",
+ " 0.875000 | \n",
+ " 1.024695 | \n",
+ " -0.343750 | \n",
+ " 2.256241 | \n",
+ " -1.187500 | \n",
+ " 3.187868 | \n",
+ "
\n",
+ " \n",
+ " | 178 | \n",
+ " 0.006700 | \n",
+ " 1.125000 | \n",
+ " 2.250000 | \n",
+ " 196.187500 | \n",
+ " 82.000000 | \n",
+ " 256.000000 | \n",
+ " 0.250000 | \n",
+ " 176.250000 | \n",
+ " 82.000000 | \n",
+ " 239.000000 | \n",
+ " 0.809019 | \n",
+ " 1.500000 | \n",
+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -1.250000 | \n",
+ " 3.820995 | \n",
+ "
\n",
+ " \n",
+ " | 179 | \n",
+ " 0.006300 | \n",
+ " 1.125000 | \n",
+ " 1.590990 | \n",
+ " 197.250000 | \n",
+ " 110.000000 | \n",
+ " 256.000000 | \n",
+ " 0.375000 | \n",
+ " 162.000000 | \n",
+ " 110.000000 | \n",
+ " 253.000000 | \n",
+ " 0.703606 | \n",
+ " 1.250000 | \n",
+ " 1.000000 | \n",
+ " 0.312500 | \n",
+ " 2.250000 | \n",
+ " -0.437500 | \n",
+ " 3.881044 | \n",
+ "
\n",
+ " \n",
+ " | 180 | \n",
+ " 0.018000 | \n",
+ " 2.250000 | \n",
+ " 1.299038 | \n",
+ " 199.625000 | \n",
+ " 80.000000 | \n",
+ " 256.000000 | \n",
+ " 0.250000 | \n",
+ " 180.833344 | \n",
+ " 80.000000 | \n",
+ " 240.000000 | \n",
+ " 0.674449 | \n",
+ " 1.500000 | \n",
+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -0.125000 | \n",
+ " 4.177320 | \n",
+ "
\n",
+ " \n",
+ " | 181 | \n",
+ " -0.008400 | \n",
+ " 3.656250 | \n",
+ " 3.937500 | \n",
+ " 210.687500 | \n",
+ " 158.000000 | \n",
+ " 256.000000 | \n",
+ " 0.062500 | \n",
+ " 207.666672 | \n",
+ " 158.000000 | \n",
+ " 247.000000 | \n",
+ " 0.632891 | \n",
+ " 1.875000 | \n",
+ " 0.500000 | \n",
+ " 1.718750 | \n",
+ " 1.125000 | \n",
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+ " 4.523550 | \n",
+ "
\n",
+ " \n",
+ " | 182 | \n",
+ " 0.003500 | \n",
+ " -3.093750 | \n",
+ " 0.359035 | \n",
+ " 242.750000 | \n",
+ " 200.000000 | \n",
+ " 256.000000 | \n",
+ " 0.750000 | \n",
+ " 203.000000 | \n",
+ " 200.000000 | \n",
+ " 208.000000 | \n",
+ " 0.529577 | \n",
+ " 0.500000 | \n",
+ " 0.894427 | \n",
+ " -1.093750 | \n",
+ " 2.010131 | \n",
+ " -2.500000 | \n",
+ " 0.894427 | \n",
+ "
\n",
+ " \n",
+ " | 183 | \n",
+ " -0.006800 | \n",
+ " -0.843750 | \n",
+ " 0.562500 | \n",
+ " 230.437500 | \n",
+ " 148.000000 | \n",
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+ " 0.687500 | \n",
+ " 174.199997 | \n",
+ " 148.000000 | \n",
+ " 253.000000 | \n",
+ " 0.547006 | \n",
+ " 0.625000 | \n",
+ " 0.957427 | \n",
+ " -1.093750 | \n",
+ " 2.154211 | \n",
+ " -0.375000 | \n",
+ " 3.242941 | \n",
+ "
\n",
+ " \n",
+ " | 184 | \n",
+ " -0.002400 | \n",
+ " 1.125000 | \n",
+ " 3.549038 | \n",
+ " 199.875000 | \n",
+ " 109.000000 | \n",
+ " 256.000000 | \n",
+ " 0.250000 | \n",
+ " 181.166672 | \n",
+ " 109.000000 | \n",
+ " 230.000000 | \n",
+ " 0.711420 | \n",
+ " 1.500000 | \n",
+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -1.250000 | \n",
+ " 3.820995 | \n",
+ "
\n",
+ " \n",
+ " | 185 | \n",
+ " 0.027200 | \n",
+ " 0.468750 | \n",
+ " 3.139869 | \n",
+ " 234.187500 | \n",
+ " 184.000000 | \n",
+ " 256.000000 | \n",
+ " 0.437500 | \n",
+ " 217.222229 | \n",
+ " 184.000000 | \n",
+ " 256.000000 | \n",
+ " 0.605494 | \n",
+ " 1.125000 | \n",
+ " 1.024695 | \n",
+ " 0.218750 | \n",
+ " 2.205816 | \n",
+ " -0.875000 | \n",
+ " 3.612478 | \n",
+ "
\n",
+ " \n",
+ " | 186 | \n",
+ " 0.004100 | \n",
+ " -1.125000 | \n",
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+ " 180.437500 | \n",
+ " 93.000000 | \n",
+ " 256.000000 | \n",
+ " 0.250000 | \n",
+ " 155.250000 | \n",
+ " 93.000000 | \n",
+ " 208.000000 | \n",
+ " 0.889437 | \n",
+ " 1.500000 | \n",
+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -3.500000 | \n",
+ " 0.894427 | \n",
+ "
\n",
+ " \n",
+ " | 187 | \n",
+ " 0.002900 | \n",
+ " -2.250000 | \n",
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+ " 207.875000 | \n",
+ " 138.000000 | \n",
+ " 256.000000 | \n",
+ " 0.500000 | \n",
+ " 159.750000 | \n",
+ " 138.000000 | \n",
+ " 197.000000 | \n",
+ " 0.624290 | \n",
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+ " 1.032796 | \n",
+ " -0.250000 | \n",
+ " 2.323790 | \n",
+ " -3.000000 | \n",
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+ "
\n",
+ " \n",
+ " | 188 | \n",
+ " -0.007100 | \n",
+ " 0.562500 | \n",
+ " 1.125000 | \n",
+ " 217.125000 | \n",
+ " 156.000000 | \n",
+ " 256.000000 | \n",
+ " 0.500000 | \n",
+ " 178.250000 | \n",
+ " 156.000000 | \n",
+ " 209.000000 | \n",
+ " 0.560508 | \n",
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+ " 1.032796 | \n",
+ " -0.250000 | \n",
+ " 2.323790 | \n",
+ " -0.187500 | \n",
+ " 3.691770 | \n",
+ "
\n",
+ " \n",
+ " | 189 | \n",
+ " 0.009900 | \n",
+ " 0.562500 | \n",
+ " 1.125000 | \n",
+ " 194.000000 | \n",
+ " 135.000000 | \n",
+ " 256.000000 | \n",
+ " 0.250000 | \n",
+ " 173.333344 | \n",
+ " 135.000000 | \n",
+ " 239.000000 | \n",
+ " 0.743465 | \n",
+ " 1.500000 | \n",
+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -1.812500 | \n",
+ " 3.487478 | \n",
+ "
\n",
+ " \n",
+ " | 190 | \n",
+ " 0.004000 | \n",
+ " 0.843750 | \n",
+ " 1.687500 | \n",
+ " 209.187500 | \n",
+ " 166.000000 | \n",
+ " 256.000000 | \n",
+ " 0.187500 | \n",
+ " 198.384628 | \n",
+ " 166.000000 | \n",
+ " 251.000000 | \n",
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+ " 1.625000 | \n",
+ " 0.806226 | \n",
+ " 1.156250 | \n",
+ " 1.814008 | \n",
+ " -1.937500 | \n",
+ " 3.530227 | \n",
+ "
\n",
+ " \n",
+ " | 191 | \n",
+ " 0.003100 | \n",
+ " 1.125000 | \n",
+ " 0.000000 | \n",
+ " 196.687500 | \n",
+ " 122.000000 | \n",
+ " 256.000000 | \n",
+ " 0.250000 | \n",
+ " 176.916672 | \n",
+ " 122.000000 | \n",
+ " 217.000000 | \n",
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+ " 1.500000 | \n",
+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -1.250000 | \n",
+ " 3.820995 | \n",
+ "
\n",
+ " \n",
+ " | 192 | \n",
+ " 0.009000 | \n",
+ " 4.781250 | \n",
+ " 2.870513 | \n",
+ " 217.312500 | \n",
+ " 142.000000 | \n",
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+ " 0.312500 | \n",
+ " 199.727280 | \n",
+ " 142.000000 | \n",
+ " 250.000000 | \n",
+ " 0.526592 | \n",
+ " 1.375000 | \n",
+ " 0.957427 | \n",
+ " 1.156250 | \n",
+ " 1.546165 | \n",
+ " 2.250000 | \n",
+ " 3.696846 | \n",
+ "
\n",
+ " \n",
+ " | 193 | \n",
+ " 0.009000 | \n",
+ " 0.562500 | \n",
+ " 2.424038 | \n",
+ " 219.750000 | \n",
+ " 135.000000 | \n",
+ " 256.000000 | \n",
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+ " 183.500000 | \n",
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+ " 1.032796 | \n",
+ " -0.250000 | \n",
+ " 2.323790 | \n",
+ " -0.187500 | \n",
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+ "
\n",
+ " \n",
+ " | 194 | \n",
+ " 0.018200 | \n",
+ " -0.562500 | \n",
+ " 0.649519 | \n",
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+ " 136.000000 | \n",
+ " 256.000000 | \n",
+ " 0.625000 | \n",
+ " 178.666672 | \n",
+ " 136.000000 | \n",
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+ " 0.544548 | \n",
+ " 0.750000 | \n",
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+ " -0.812500 | \n",
+ " 2.250000 | \n",
+ " -0.500000 | \n",
+ " 3.346640 | \n",
+ "
\n",
+ " \n",
+ " | 195 | \n",
+ " 0.035300 | \n",
+ " 1.968750 | \n",
+ " 4.663189 | \n",
+ " 195.875000 | \n",
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+ " 0.187500 | \n",
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+ " 1.625000 | \n",
+ " 0.806226 | \n",
+ " 1.156250 | \n",
+ " 1.814008 | \n",
+ " -0.812500 | \n",
+ " 4.118556 | \n",
+ "
\n",
+ " \n",
+ " | 196 | \n",
+ " 0.003300 | \n",
+ " 4.500000 | \n",
+ " 3.073557 | \n",
+ " 186.562500 | \n",
+ " 133.000000 | \n",
+ " 256.000000 | \n",
+ " 0.125000 | \n",
+ " 176.642868 | \n",
+ " 133.000000 | \n",
+ " 256.000000 | \n",
+ " 0.713881 | \n",
+ " 1.750000 | \n",
+ " 0.683130 | \n",
+ " 1.437500 | \n",
+ " 1.537043 | \n",
+ " 1.312500 | \n",
+ " 4.362243 | \n",
+ "
\n",
+ " \n",
+ " | 197 | \n",
+ " -0.002800 | \n",
+ " -1.968750 | \n",
+ " 0.562500 | \n",
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+ " 0.437500 | \n",
+ " 203.777786 | \n",
+ " 153.000000 | \n",
+ " 251.000000 | \n",
+ " 0.684032 | \n",
+ " 1.125000 | \n",
+ " 1.024695 | \n",
+ " 0.031250 | \n",
+ " 2.305564 | \n",
+ " -3.125000 | \n",
+ " 1.024695 | \n",
+ "
\n",
+ " \n",
+ " | 198 | \n",
+ " 0.003400 | \n",
+ " 1.125000 | \n",
+ " 0.000000 | \n",
+ " 187.625000 | \n",
+ " 113.000000 | \n",
+ " 256.000000 | \n",
+ " 0.250000 | \n",
+ " 164.833344 | \n",
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+ " 1.500000 | \n",
+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -1.250000 | \n",
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+ "
\n",
+ " \n",
+ " | 199 | \n",
+ " 0.008700 | \n",
+ " 2.062500 | \n",
+ " 1.515544 | \n",
+ " 203.625000 | \n",
+ " 124.000000 | \n",
+ " 256.000000 | \n",
+ " 0.375000 | \n",
+ " 172.199997 | \n",
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+ " 250.000000 | \n",
+ " 0.784189 | \n",
+ " 1.250000 | \n",
+ " 1.000000 | \n",
+ " 0.687500 | \n",
+ " 1.965324 | \n",
+ " 0.125000 | \n",
+ " 3.981206 | \n",
+ "
\n",
+ " \n",
+ " | 200 | \n",
+ " 0.029800 | \n",
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+ " 2.337019 | \n",
+ " 209.812500 | \n",
+ " 142.000000 | \n",
+ " 256.000000 | \n",
+ " 0.187500 | \n",
+ " 199.153854 | \n",
+ " 142.000000 | \n",
+ " 249.000000 | \n",
+ " 0.639061 | \n",
+ " 1.625000 | \n",
+ " 0.806226 | \n",
+ " 1.156250 | \n",
+ " 1.814008 | \n",
+ " 0.312500 | \n",
+ " 4.331570 | \n",
+ "
\n",
+ " \n",
+ " | 201 | \n",
+ " 0.040000 | \n",
+ " -1.875000 | \n",
+ " 0.306186 | \n",
+ " 193.187500 | \n",
+ " 108.000000 | \n",
+ " 256.000000 | \n",
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+ " 130.375000 | \n",
+ " 108.000000 | \n",
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+ " 0.768529 | \n",
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+ " 1.032796 | \n",
+ " 0.125000 | \n",
+ " 2.085665 | \n",
+ " -3.000000 | \n",
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+ "
\n",
+ " \n",
+ " | 202 | \n",
+ " 0.003000 | \n",
+ " 2.250000 | \n",
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+ " 187.000000 | \n",
+ " 256.000000 | \n",
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+ " 206.250000 | \n",
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+ " 254.000000 | \n",
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+ " 1.032796 | \n",
+ " -0.250000 | \n",
+ " 2.323790 | \n",
+ " 1.500000 | \n",
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+ "
\n",
+ " \n",
+ " | 203 | \n",
+ " 0.002700 | \n",
+ " -2.250000 | \n",
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+ " 153.000000 | \n",
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+ " -0.250000 | \n",
+ " 2.323790 | \n",
+ " -3.000000 | \n",
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+ "
\n",
+ " \n",
+ " | 204 | \n",
+ " 0.023700 | \n",
+ " 1.875000 | \n",
+ " 2.250000 | \n",
+ " 233.187500 | \n",
+ " 186.000000 | \n",
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+ " -0.062500 | \n",
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+ "
\n",
+ " \n",
+ " | 205 | \n",
+ " 0.003200 | \n",
+ " -2.250000 | \n",
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+ " 150.000000 | \n",
+ " 256.000000 | \n",
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+ " -0.250000 | \n",
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+ "
\n",
+ " \n",
+ " | 206 | \n",
+ " 0.026900 | \n",
+ " 0.562500 | \n",
+ " 3.375000 | \n",
+ " 197.687500 | \n",
+ " 121.000000 | \n",
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+ " 162.699997 | \n",
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+ " 0.312500 | \n",
+ " 2.250000 | \n",
+ " -1.000000 | \n",
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\n",
+ " \n",
+ " | 207 | \n",
+ " 0.015700 | \n",
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+ " 3.375000 | \n",
+ " 219.812500 | \n",
+ " 162.000000 | \n",
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+ " 207.750000 | \n",
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+ " 1.500000 | \n",
+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -1.250000 | \n",
+ " 3.820995 | \n",
+ "
\n",
+ " \n",
+ " | 208 | \n",
+ " 0.042700 | \n",
+ " 0.187500 | \n",
+ " 2.883906 | \n",
+ " 208.750000 | \n",
+ " 133.000000 | \n",
+ " 256.000000 | \n",
+ " 0.437500 | \n",
+ " 172.000000 | \n",
+ " 133.000000 | \n",
+ " 250.000000 | \n",
+ " 0.591297 | \n",
+ " 1.250000 | \n",
+ " 1.000000 | \n",
+ " 0.500000 | \n",
+ " 2.121320 | \n",
+ " -1.562500 | \n",
+ " 3.385632 | \n",
+ "
\n",
+ " \n",
+ " | 209 | \n",
+ " 0.020200 | \n",
+ " -0.468750 | \n",
+ " 1.312500 | \n",
+ " 216.875000 | \n",
+ " 106.000000 | \n",
+ " 256.000000 | \n",
+ " 0.500000 | \n",
+ " 177.750000 | \n",
+ " 106.000000 | \n",
+ " 226.000000 | \n",
+ " 0.673544 | \n",
+ " 1.000000 | \n",
+ " 1.032796 | \n",
+ " -0.156250 | \n",
+ " 2.256241 | \n",
+ " -1.312500 | \n",
+ " 3.260240 | \n",
+ "
\n",
+ " \n",
+ " | 210 | \n",
+ " 0.009700 | \n",
+ " -1.125000 | \n",
+ " 1.299038 | \n",
+ " 226.625000 | \n",
+ " 146.000000 | \n",
+ " 256.000000 | \n",
+ " 0.500000 | \n",
+ " 197.250000 | \n",
+ " 146.000000 | \n",
+ " 241.000000 | \n",
+ " 0.640770 | \n",
+ " 1.000000 | \n",
+ " 1.032796 | \n",
+ " -0.250000 | \n",
+ " 2.323790 | \n",
+ " -1.875000 | \n",
+ " 2.848976 | \n",
+ "
\n",
+ " \n",
+ " | 211 | \n",
+ " -0.011300 | \n",
+ " 2.812500 | \n",
+ " 1.125000 | \n",
+ " 172.375000 | \n",
+ " 128.000000 | \n",
+ " 248.000000 | \n",
+ " 0.000000 | \n",
+ " 172.375000 | \n",
+ " 128.000000 | \n",
+ " 248.000000 | \n",
+ " 0.880617 | \n",
+ " 2.000000 | \n",
+ " 0.000000 | \n",
+ " 2.000000 | \n",
+ " 0.000000 | \n",
+ " -1.187500 | \n",
+ " 4.308422 | \n",
+ "
\n",
+ " \n",
+ " | 212 | \n",
+ " -0.007500 | \n",
+ " 2.250000 | \n",
+ " 1.299038 | \n",
+ " 226.750000 | \n",
+ " 138.000000 | \n",
+ " 256.000000 | \n",
+ " 0.312500 | \n",
+ " 213.454559 | \n",
+ " 138.000000 | \n",
+ " 253.000000 | \n",
+ " 0.589944 | \n",
+ " 1.500000 | \n",
+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -0.125000 | \n",
+ " 4.177320 | \n",
+ "
\n",
+ " \n",
+ " | 213 | \n",
+ " 0.021400 | \n",
+ " -0.093750 | \n",
+ " 0.187500 | \n",
+ " 226.250000 | \n",
+ " 162.000000 | \n",
+ " 256.000000 | \n",
+ " 0.562500 | \n",
+ " 188.000015 | \n",
+ " 162.000000 | \n",
+ " 218.000000 | \n",
+ " 0.581749 | \n",
+ " 0.875000 | \n",
+ " 1.024695 | \n",
+ " -0.343750 | \n",
+ " 2.256241 | \n",
+ " -0.625000 | \n",
+ " 3.442383 | \n",
+ "
\n",
+ " \n",
+ " | 214 | \n",
+ " 0.060900 | \n",
+ " 2.156250 | \n",
+ " 2.511057 | \n",
+ " 205.625000 | \n",
+ " 126.000000 | \n",
+ " 256.000000 | \n",
+ " 0.312500 | \n",
+ " 182.727280 | \n",
+ " 126.000000 | \n",
+ " 223.000000 | \n",
+ " 0.714258 | \n",
+ " 1.375000 | \n",
+ " 0.957427 | \n",
+ " 0.781250 | \n",
+ " 1.991388 | \n",
+ " 0.000000 | \n",
+ " 4.082483 | \n",
+ "
\n",
+ " \n",
+ " | 215 | \n",
+ " 0.022900 | \n",
+ " 3.281250 | \n",
+ " 3.149143 | \n",
+ " 231.062500 | \n",
+ " 167.000000 | \n",
+ " 256.000000 | \n",
+ " 0.187500 | \n",
+ " 225.307693 | \n",
+ " 167.000000 | \n",
+ " 256.000000 | \n",
+ " 0.508764 | \n",
+ " 1.625000 | \n",
+ " 0.806226 | \n",
+ " 1.343750 | \n",
+ " 1.546165 | \n",
+ " 0.312500 | \n",
+ " 4.331570 | \n",
+ "
\n",
+ " \n",
+ " | 216 | \n",
+ " 0.003800 | \n",
+ " -1.125000 | \n",
+ " 0.000000 | \n",
+ " 175.437500 | \n",
+ " 98.000000 | \n",
+ " 256.000000 | \n",
+ " 0.250000 | \n",
+ " 148.583344 | \n",
+ " 98.000000 | \n",
+ " 215.000000 | \n",
+ " 0.814252 | \n",
+ " 1.500000 | \n",
+ " 0.894427 | \n",
+ " 0.875000 | \n",
+ " 2.012461 | \n",
+ " -3.500000 | \n",
+ " 0.894427 | \n",
+ "
\n",
+ " \n",
+ " | 217 | \n",
+ " -0.006900 | \n",
+ " -0.843750 | \n",
+ " 1.415594 | \n",
+ " 217.000000 | \n",
+ " 149.000000 | \n",
+ " 256.000000 | \n",
+ " 0.312500 | \n",
+ " 199.272736 | \n",
+ " 149.000000 | \n",
+ " 251.000000 | \n",
+ " 0.632158 | \n",
+ " 1.375000 | \n",
+ " 0.957427 | \n",
+ " 0.593750 | \n",
+ " 2.154211 | \n",
+ " -2.812500 | \n",
+ " 2.286737 | \n",
+ "
\n",
+ " \n",
+ " | 218 | \n",
+ " 0.010100 | \n",
+ " 2.343750 | \n",
+ " 2.812500 | \n",
+ " 213.375000 | \n",
+ " 154.000000 | \n",
+ " 256.000000 | \n",
+ " 0.312500 | \n",
+ " 194.000000 | \n",
+ " 154.000000 | \n",
+ " 245.000000 | \n",
+ " 0.663645 | \n",
+ " 1.375000 | \n",
+ " 0.957427 | \n",
+ " 0.968750 | \n",
+ " 1.793216 | \n",
+ " 0.000000 | \n",
+ " 4.082483 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same material as the brown cube. First, I need to identify the material of the brown object mentioned in the scene.\n",
+ "\n",
+ "Looking at the image provided, there are three objects: a large yellow metal sphere, a small purple rubber sphere, and a small brown metal cube. The question refers to the \"brown thing,\" which corresponds to the third object listed: the small brown metal cube. \n",
+ "\n",
+ "Now, I need to check if any other objects have the same material. Let me go through each object one by one:\n",
+ "\n",
+ "1. The first object is a large yellow metal sphere. Its material is metal. Since the brown object is also made of metal, this counts as one match.\n",
+ "2. The second object is a small purple rubber sphere. The material here is rubber, so that doesn given object does not match.\n",
+ "3. The third object is a small brown metal cube. This is the one we're comparing against. Since its material is metal, it matches the brown object.\n",
+ "\n",
+ "So, the brown cube is made of metal. Now, checking the other two objects:\n",
+ "- The first object is metal (matches), but the second is rubber (doesn't match). So only one other\n",
+ "predicted:None\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each entry one by one.\n",
+ "\n",
+ "First line: \"A large cyan rubber cylinder...\" That's one item. So that's 1.\n",
+ "\n",
+ "Second line: \"A small yellow metal sphere...\" That's another item. Total now 2.\n",
+ "\n",
+ "Third line: \"A large blue rubber cube...\" Third item. Now 3.\n",
+ "\n",
+ "Fourth line: \"A small gray rubber cube...\" Fourth item. Now 4.\n",
+ "\n",
+ "Fifth line: \"A small red metal cylinder...\" Fifth item. Now 5.\n",
+ "\n",
+ "Sixth line: \"A large green rubber cylinder...\" Sixth item. Now 6.\n",
+ "\n",
+ "Seventh line: \"A large red rubber sphere...\" Seventh item. Now 7.\n",
+ "\n",
+ "So each line represents a separate object with different attributes like color, material, shape, size, rotation, coordinates, and pixel coordinates. Each of these entries is a distinct item. There are no mentions of multiple objects being part of the same entity or any overlap mentioned. Therefore, the total number should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I need to count each distinct objects mentioned in the scene. Each entry starts with \"A large cyan rubber sphere...\" followed by its attributes. There are six such entries here. Each of these entries describes a separate object. \n",
+ "\n",
+ "Let me list them out again for clarity:\n",
+ "\n",
+ "1. A large cyan rubber sphere rotated 235.7° located at 3D coordinates (-0.89, -2.64, 0.70) and pixel coordinates (220, 105)\n",
+ "2. A small blue rubber cube rotated 232.2° located at 3D (1.52, -2.33, 0.35) and pixel (300, 177)\n",
+ "3. A large yellow metal sphere rotated 233.2° located at 3D (2.83, 1.80, 0.70) and pixel (192, 117)\n",
+ "4. A large gray metal sphere rotated 2\n",
+ "predicted:None\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many objects meet two conditions: being \"big\" and \"shiny\" AND being in front of the \"large gray metal cube\". Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to identify all the objects that are \"big\" and then check if they are \"metallic\" and located in front of the large gray metal cube.\n",
+ "\n",
+ "Looking at the scene:\n",
+ "\n",
+ "1. **Large blue rubber sphere**: Size: large, color: blue, material: rubber. Not big, so not relevant.\n",
+ "2. **Small gray metal cube**: Small size, so not big. Also, it's a cube, but the question is for big objects. So no.\n",
+ "3. **Large cyan metal cylinder**: Large, color cyan, material metal. But its position is behind the large gray metal cube. Since the question is about objects in front of the large gray metal cube, this one doesn given its position is behind, so it doesn't count.\n",
+ "4. **Large red metal sphere**: Large, color red, material metal. This is a sphere, which is a different shape than the cube mentioned in the question. However, since the question is about\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I need to check each entry in the image provided. Each entry starts with \"A small brown rubber cube...\" followed by its attributes like size, color, material, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Looking at each line:\n",
+ "\n",
+ "1. A small brown rubber cube rotated 205.6° located at certain coordinates. That's one item.\n",
+ "2. A large red rubber sphere rotated 147.8° at different coordinates. Second item.\n",
+ "3. A large cyan metal cylinder rotated 29.3° with specific coordinates. Third item.\n",
+ "4. A large purple metal sphere rotated 233.2° with different coordinates. Fourth item.\n",
+ "5. A large green metal cylinder rotated 23.7° with different coordinates. Fifth item.\n",
+ "\n",
+ "Each of these entries describes a separate object. There are five distinct objects listed here. Even though some have similar properties (like both being cylinders or spheres), they are all different in their attributes. So each is a separate item. Therefore, the total number should be five.\n",
+ "\n",
+ "predicted:None\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of big purple cylinders is less than the number of red cubes. Let me look at each object one by one.\n",
+ "\n",
+ "First, I need to count the number of big purple cylinders. Let's check each item in the scene:\n",
+ "\n",
+ "Looking at the first line: \"A large purple metal sphere...\" So that's one big purple object. But wait, the question is about cylinders, not spheres. The objects listed here are a sphere (which is a type of ball), so this doesn given isn't a cylinder. So that's zero.\n",
+ "\n",
+ "Next, the second line: \"A small brown rubber sphere...\" Again, another sphere. No cylinder here. So that's zero again.\n",
+ "\n",
+ "Third line: \"A small green rubber cylinder...\" That's a cylinder, but it's green, not purple. So that's one. \n",
+ "\n",
+ "Fourth line: \"A small gray metal cube...\" Cube, not cylinder. So that's zero.\n",
+ "\n",
+ "Fifth line: \"A large brown metal sphere...\" Sphere again, not a cylinder. Zero.\n",
+ "\n",
+ "Sixth line: \"A small yellow rubber cube...\" Cube, not cylinder. Zero.\n",
+ "\n",
+ "So adding them up, there are 1 big purple cylinder and 0 red cubes.\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of large cyan cylinders that have the same material as the small blue object. First, I need to parse through each object in the scene to figure out their properties.\n",
+ "\n",
+ "Looking at the first line: \"A small brown metal cube...\" So the material here is metal. The size is small, color is brown. Next line: \"A large red metal cylinder...\" Material again is metal, but size is large, color red. Third line: \"A small blue rubber cube...\" So here, the material is rubber, which is different from the first two objects. The fourth line: \"A large cyan rubber cylinder...\" This one has a cyan color and is made of rubber. Now, the key part is comparing this last object's material (rubber) with the small blue object's material (rubber). Since both are rubber, they match. But then, the question asks for large cyan cylinders. The last object is a large cyan rubber cylinder, so it fits both criteria. Are there any other large cyan cylinders in the scene?\n",
+ "\n",
+ "Let me check all the objects again. The first three are small or large, but none are large cyan cylinders. The fourth is a large cyan rubber cylinder,\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many large matte things are right of the specified object. First, I need to parse through the image provided.\n",
+ "\n",
+ "The scene has three objects: two green cylinders and one red metal sphere. Let me list them out:\n",
+ "\n",
+ "1. A large green rubber cylinder rotated 235.0° located at 3D coordinates (1.79, -2.86, 0.70) and pixel coordinates (343, 130, 10.00).\n",
+ "2. Another large green rubber cylinder rotated 235.0° at 3D (2.82, -0.92, 0.70) and pixel (371, 130, 10.00).\n",
+ "3. A small red metal sphere rotated 235.0° at 3D (-1.93, 1.93, 0.35) and pixel (291, 130, 10.00).\n",
+ "\n",
+ "Now, the key part is finding the \"green matte object\" mentioned in the question. Since both green cylinders are made of rubber (mat\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many cylinders are either green objects or large matte things. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to identify all the objects in the scene and check their properties. The scene lists several items with different attributes like color, material, shape, size, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Looking at the first object: a large purple metal cylinder. Its color is purple, so that's not green. Size is large, but the question is about green objects or large matte things. Since the material here is metal, which is not matte, this doesn given criteria. So this one doesn't count.\n",
+ "\n",
+ "Next, the second object is a small yellow rubber cube. Color is yellow, so not green. Size is small, so not large. Material is rubber, which is matte. So this one also doesn't meet the criteria.\n",
+ "\n",
+ "Third is a large yellow rubber cube. Again, color is yellow, so not green. Size is large, but material is rubber (matte). So this one fits the \"large matte\" part, but not the \"green\". So no.\n",
+ "\n",
+ "Fourth is a large purple rubber sphere. Color\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I need to count each distinct item mentioned. Each entry starts with \"A small blue metal cube...\" followed by its attributes like color, size, rotation, coordinates, and pixel coordinates. \n",
+ "\n",
+ "Looking at the first line: \"A small blue metal cube rotated 123.7° located at 3D coordinates (-0.59, -2.86, 0.40) and pixel (120, 100)\". That's one item. Next, the second line is another cube, different color and material. So that's two items. The third is a large gray metal cube, so that's three. The fourth is a small red rubber sphere, which is the fourth item. So adding them up: 1 + 1 + 1 + 1 = 4. Therefore, there should be four items in total.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many purple things are either shiny balls or matte cylinders. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to check each item in the scene for both categories: \"shiny balls\" and \"matte cylinders\". \n",
+ "\n",
+ "Starting with the first object: a large yellow rubber cube. The color here is yellow, so that's not purple. It's a cube, which is a shape, not a ball. So this doesn given category is neither a ball nor a cylinder. So no contribution here.\n",
+ "\n",
+ "Second object: a small cyan metal cylinder. The color is cyan, which is not purple. Shape is cylinder, but the material is metal, which is shiny. So this also doesn't fit either category. So zero contributions here.\n",
+ "\n",
+ "Third object: a large green rubber cylinder. Green, so not purple. Cylinder again, but the material is rubber, which is matte. This is a cylinder, but the color is green, so it's not purple. So no.\n",
+ "\n",
+ "Fourth object: a large blue metal sphere. Blue, so not purple. Sphere (ball) and metal (shiny), so this counts as a shiny ball. That's one\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each entry one by one.\n",
+ "\n",
+ "First line: \"A small green metal cylinder...\" That's one item. Next, another \"small green metal cylinder\" – that's two items. Then a \"large gray metal cube\"... third item. Then a \"large red rubber sphere\"... fourth. Then a \"small gray rubber sphere\"... fifth. Then a \"large yellow metal cube\"... sixth. Finally, a \"large green rubber sphere\"... seventh. \n",
+ "\n",
+ "Each of these entries starts with \"A\" or \"A large\", which indicates they're individual objects. There are seven distinct entries here. Each has different attributes like color, size, material, shape, rotation, coordinates, and pixel coordinates. Since each entry is a separate object, even if some have similar properties (like both being spheres), they are still counted as separate items. So adding them up, there should be seven items in total.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking for the number of objects that are either cylinders or large purple matte spheres. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to look at each item in the scene provided. Each entry starts with \"A small yellow rubber sphere...\" followed by its attributes. Then there's another line starting with \"A large purple metal cylinder...\" and then another \"A large yellow rubber cube...\". \n",
+ "\n",
+ "Let's check each object:\n",
+ "\n",
+ "1. **Small yellow rubber sphere**: This is a sphere, so its color is yellow, material is rubber, size is small. Since the question asks for cylinders or large purple matte spheres, this doesn given here is not relevant because it's a sphere, not a cylinder. So no.\n",
+ "\n",
+ "2. **Large purple metal cylinder**: This is a cylinder. Its color is purple and it's made of metal. The question mentions both cylinders (cylinder) and large purple matte spheres. The material here is metal, which is different from matte. So this one also doesn't fit.\n",
+ "\n",
+ "3. **Large yellow rubber cube**: This is a cube, so it's a different shape. Even though it's large and yellow, the material is\n",
+ "predicted:None\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same material as the \"tiny yellow rubber cube\". First, I need to identify which object matches this description.\n",
+ "\n",
+ "Looking at the image provided, there are three objects listed: a small brown metal sphere, a large red rubber sphere, and a small yellow rubber cube. The key here is to determine if any other objects have the same material as the \"tiny yellow block\" (which should be the yellow rubber cube in the scene).\n",
+ "\n",
+ "The material of the cube is specified as \"rubber\". So, I need to check each object for their material. Let me go through each one:\n",
+ "\n",
+ "1. The first object is a small yellow metal sphere. Its material is metal, so that doesn given material is different from rubber. So this one doesn't match.\n",
+ "2. The second object is a large red rubber sphere. This has rubber as its material, which is the same as the cube. So this is a match.\n",
+ "3. The third object is another small yellow metal sphere. Again, metal, so not a match.\n",
+ "\n",
+ "So, only the second object shares the same material as the yellow cube. Therefore, there is 1 other object with the same material.\n",
+ " 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I need to check each entry to see if they're separate objects. Each entry starts with \"A small cyan metal sphere...\" or \"A large yellow rubber sphere...\" followed by their coordinates and rotation. \n",
+ "\n",
+ "Looking through all five entries:\n",
+ "\n",
+ "1. A small brown rubber sphere: This is one object.\n",
+ "2. A large gray metal cube: That's another one.\n",
+ "3. A small green rubber cylinder: Third item.\n",
+ "4. A large yellow rubber sphere: Fourth item.\n",
+ "5. A small blue rubber cube: Fifth item.\n",
+ "5. A small cyan metal sphere: Sixth item.\n",
+ "\n",
+ "Each of these entries describes a distinct object with different attributes like color, size, material, shape, rotation, and coordinates. Since each has unique properties and positions, each is a separate item. There are no mentions of multiple instances of any single object. Therefore, the total number should be 5.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many objects are either rubber balls that are behind the small gray rubber sphere or large spheres.\n",
+ "\n",
+ "First, I need to parse the scene described in the image provided. There are three objects listed here: two spheres and one cube. Let me list them out:\n",
+ "\n",
+ "1. A small red rubber sphere rotated 235.0° located at 3D coordinates (0.79, -2.86, 0.40) and pixel coordinates (111.30, 111.30).\n",
+ "2. A large blue rubber sphere rotated 15.0° located at 2.00, 0.70, 0.35 and pixel coordinates (192.00, 192.00).\n",
+ "3. A small gray rubber sphere rotated 270.0° located at 0.15, -1.50, 0.40 and pixel coordinates (101.00, 101.00).\n",
+ "\n",
+ "Now, the key part of the question is to find objects that are either rubber balls (spheres)\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's tackle this question step by step. The user is asking if the number of metal balls that are behind the big green metallic object is greater than the number of green metallic spheres that are behind the tiny green shiny sphere.\n",
+ "\n",
+ "First, I need to parse through the image provided. There are three objects listed here:\n",
+ "\n",
+ "1. A small red metal sphere rotated 235.0° located at 3D coordinates (-0.47, -2.69, 0.35) and pixel coordinates (182, 110, 10.50).\n",
+ "2. A large green metal sphere rotated 153.0° at 3D (2.90, 2.40, 0.75) and pixel (352, 150, 10.50).\n",
+ "3. A small green metal sphere rotated 233.0° at 3D (-1.90, -1.80, 0.35) and pixel (292, 130, 10.50).\n",
+ "\n",
+ "The question has two parts: comparing the number of metal balls behind the big green metallic\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many rubber cylinders have the same size as the green matte thing. First, I need to figure out what the \"green matte thing\" refers to in the scene.\n",
+ "\n",
+ "Looking at the image provided, there are four objects listed. Let me go through each one:\n",
+ "\n",
+ "1. A large cyan metal cylinder rotated 237.4° located at certain coordinates. Its material is metal, color cyan, and shape is cylinder. So this doesn't match the criteria since it's a metal cylinder, not rubber.\n",
+ "\n",
+ "2. A small gray metal cube with rotation 296.0° and coordinates. Again, metal cube, so not relevant here.\n",
+ "\n",
+ "3. A large cyan rubber cylinder with rotation 258.3° and coordinates. This one has a rubber material, but its color is cyan, which isn't green. So this is irrelevant too.\n",
+ "\n",
+ "4. A large green rubber sphere with rotation 29.0° and coordinates. The question mentions a \"green matte thing,\" which might be referring to the fourth object because it's green and made of rubber. But the material here is rubber, not matte. Matte usually implies a non-shiny surface, but the term \"mat\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of objects that are either green or big brown spheres. Let me look at each object one by one.\n",
+ "\n",
+ "First, I need to check each object in the scene provided. There are three objects listed here: two small brown cubes and one large green cylinder. \n",
+ "\n",
+ "Looking at the first object: It's a small brown metal cube. The color is brown, which is not green. So this doesn given object does not meet either condition. Next, the second object is also a small brown metal cube. Again, the color is brown, so it's not green. So this one also doesn't qualify. Finally, the third object is a large green metal cylinder. This one has a green color, but it's a cylinder, not a sphere. Therefore, even though it's green, its shape is different from a sphere. So this one is excluded because it's a cylinder.\n",
+ "\n",
+ "So, adding up all the objects that fit either category (green or big brown spheres), there's only one object that fits: the green cylinder. The other two are both brown cubes, neither of which are green or brown spheres. Therefore, the answer should be 1.\n",
+ " 1\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at the image provided.\n",
+ "\n",
+ "First, I need to check each entry listed here. Each entry starts with \"A small blue metal sphere...\" followed by its attributes. There's another entry: \"A large brown metal cube...\" and then a third one: \"A large green metal cylinder...\". \n",
+ "\n",
+ "Each of these entries describes a separate object. The first is a small blue metal sphere, the second is a large brown metal cube, and the third is a large green metal cylinder. Each of these objects has distinct properties like color, size, material, shape, rotation, 3D coordinates, and pixel coordinates. Since each entry is a separate item, they are three different objects. Therefore, the total number should be three.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I need to count each object mentioned in the scene. Each entry starts with \"A small gray rubber cube...\" or \"A large yellow metal sphere...\" followed by their attributes like color, size, material, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Looking through all five entries:\n",
+ "\n",
+ "1. A small gray rubber cube: That's one item.\n",
+ "2. A large cyan rubber sphere: Second item.\n",
+ "3. A small gray rubber cube: Third item.\n",
+ "4. A small cyan metal cylinder: Fourth item.\n",
+ "5. A large yellow metal sphere: Fifth item.\n",
+ "\n",
+ "Each of these entries is a separate object. There are no mentions of multiple instances of any single object. So each entry is a distinct item. Therefore, counting them individually gives us five items total.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 6 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many cylinders are either tiny things or large gray things. So I need to go through each object listed and check if they fit either category.\n",
+ "\n",
+ "First, let me list all the objects again:\n",
+ "\n",
+ "1. A small yellow metal cube rotated 235.0° located at 3D coordinates (-0.79, -2.64, 0.35) and pixel coordinates (181, 111, 10.50)\n",
+ "2. A large cyan metal sphere rotated 193.0° at 3D (2.90, 0.70, 0.75), pixel (376, 177, 10.50)\n",
+ "3. A small red metal cylinder rotated 232.0° at 3D (1.70, -2.00, 0.35), pixel (292, 160, 10.50)\n",
+ "4. A small green metal cylinder rotated 233.0° at 3D (-0.30, -2.60, 0.3\n",
+ "predicted:None\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many metal things are either large gray cylinders or large gray balls. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to check each item in the scene for their properties. The objects listed are:\n",
+ "\n",
+ "1. A small red metal cylinder rotated 243.5° located at certain coordinates.\n",
+ "2. A large green rubber sphere with rotation 167.0° and coordinates.\n",
+ "3. A large gray rubber sphere with rotation 198.2° and coordinates.\n",
+ "4. A large green rubber cylinder with rotation 10.7° and coordinates.\n",
+ "\n",
+ "Now, I'll look for \"metal\" materials. Metal would be the material listed as \"metal\" here. So the first object is made of metal (small red cylinder), but its color is red, so that doesn't count. The other three objects are either rubber or green. So there are zero metal objects.\n",
+ "\n",
+ "Next, I need to check if any of these are large gray cylinders. The third object is a large gray rubber sphere. Since it's a sphere, not a cylinder, this doesn't qualify. The others are either cylinders or spheres, but none are cylinders. Therefore, there are zero\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking for the number of objects that are either gray balls or small green cylinders. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to check each item in the scene to see if they meet either of these two criteria: being a gray ball or a small green cylinder.\n",
+ "\n",
+ "Starting with the first object: a large cyan metal cylinder. The color is cyan, so not green. The shape is a cylinder, but the size is large, so it doesn't qualify as small. So this doesn't count.\n",
+ "\n",
+ "Second object: a small yellow cube. The color is yellow, which isn't green. The shape is a cube, so it's not a cylinder. Therefore, no.\n",
+ "\n",
+ "Third object: a large gray rubber cube. The color is gray, but the shape is a cube, so it's not a ball. So this also doesn't qualify.\n",
+ "\n",
+ "Fourth: a small red rubber sphere. The color is red, so it's not green. The shape is a sphere, which is a ball. Since it's a sphere (which is a type of ball), this counts. But the color is red, so it's not gray. So this doesn't\n",
+ "predicted:None\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at the image provided.\n",
+ "\n",
+ "First, I need to check each entry listed here. Each entry starts with \"A small green metal sphere...\" followed by its attributes like color, size, rotation, coordinates, and pixel coordinates. Then there's another line that says \"A large brown metal cube...\" and then another one for a \"small purple rubber cylinder.\"\n",
+ "\n",
+ "Each of these entries seems to be separate objects. The first is a sphere, the second a cube, and the third a cylinder. Since each has distinct properties (different colors, materials, shapes, sizes, rotations, and coordinates), they are three different items. There are no mentions of any other objects mentioned. So the total number should be three.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The user provided three objects described in the scene. Let me check each one to determine how many items they represent.\n",
+ "\n",
+ "First object: A large yellow metal cube rotated 165.2° located at certain coordinates. The size is large, material is metal, color is yellow, and rotation is 165.2°. This is a single item. \n",
+ "\n",
+ "Second object: A small green rubber cylinder with a different set of coordinates. The size is small, material is rubber, color is green, and rotation is 307.9°. That's another item.\n",
+ "\n",
+ "Third object: A large yellow rubber sphere. Size is large, material is rubber, color is yellow, and rotation is 184.7°. Third item.\n",
+ "\n",
+ "Each of these entries has distinct attributes and coordinates, so each is a separate object. There are three distinct items listed here. Therefore, the total number should be three.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry in the image. Each entry starts with \"A small cyan metal sphere...\" followed by its attributes like color, size, material, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Starting with the first item: A small cyan metal sphere rotated 215.0° located at certain coordinates. That's one item. Next, a large brown rubber cube. That's two items. Then another small cyan metal sphere, different coordinates. Third is a large brown metal cube. Fourth is a small yellow metal sphere. Fifth is a large brown rubber sphere. So that's five items total. The sixth is a small cyan rubber cylinder. So adding all of these up, the total should be six items. Let me check again to make sure there are no duplicates or missed entries. Each entry is distinct based on their properties. Therefore, the answer should be six.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are fewer cyan metallic objects than the number of other objects. First, I need to count all the objects in the scene and then check their properties.\n",
+ "\n",
+ "Looking at each object one by one:\n",
+ "\n",
+ "1. A small yellow rubber cube: color is yellow, material is rubber, size small. Not relevant since it's not cyan or metallic.\n",
+ "2. Small red metal cylinder: color red, material metal, so this counts as a metallic object. So that's 1.\n",
+ "3. Large yellow rubber sphere: color yellow, material rubber. Not relevant.\n",
+ "4. Small cyan metal cylinder: color cyan, material metal. This is another metallic object. So now we have two metallic objects.\n",
+ "5. Small blue rubber cube: color blue, material rubber. Not relevant.\n",
+ "6. Large cyan rubber sphere: color cyan, material rubber. Not counted here because it's rubber.\n",
+ "7. Small yellow rubber sphere: color yellow, material rubber. Again, not relevant.\n",
+ "\n",
+ "So, counting the metallic objects: there are two (the first and third items). Now, the question is whether the number of cyan metallic objects is less than the total number of other objects. The total number of objects is 7. So\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many small blue shiny blocks are in front of the tiny metal cube. So first, I need to parse through the image provided.\n",
+ "\n",
+ "Looking at each object listed:\n",
+ "\n",
+ "1. A large blue metal sphere: This is a sphere, not a block. Its size is large, color is blue, material is metal. So this doesn't fit the criteria of being a small blue shiny block.\n",
+ "\n",
+ "2. A small green metal cylinder: Cylinder here, so not a block. Size is small, but color is green, material is metal. So again, no match.\n",
+ "\n",
+ "3. A small blue metal cube: Now this one. The cube is small, blue, made of metal. But the question is about \"small blue shiny blocks\". Wait, the cube is blue and metal, but the question says \"shiny\". Shiny implies a reflective surface, which might be implied by the material (metal) or the color (blue). However, the term \"shiny\" isn't explicitly mentioned in the scene. The objects are described as \"metal\", which could imply some level of shine, but without more context, we can't be certain. But since the cube is blue and metal, it's likely to\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at the image provided.\n",
+ "\n",
+ "First, I need to check each line of the description. Each entry starts with \"A small green rubber sphere...\" or \"A large red metal cylinder...\". Each of these describes a separate object. There are three entries here: two cylinders and one sphere. So that makes three items total. Even though one of the objects is a cylinder and the other is a sphere, they are distinct entities with different properties (one is red metal, the other green rubber). Therefore, each counts as a separate item. So the answer should be 3.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each entry one by one.\n",
+ "\n",
+ "First, I need to count all the objects listed here. Each entry starts with \"A small cyan metal cylinder...\" or similar. Let me go through each line:\n",
+ "\n",
+ "1. A small yellow rubber cube rotated 235.0° located at certain coordinates. That's one item.\n",
+ "2. A large gray metal cube rotated 167.4° at different coordinates. Second item.\n",
+ "3. A small purple metal cylinder rotated 298.2° at another set of coordinates. Third.\n",
+ "4. A small yellow metal sphere rotated 233.7°. Fourth.\n",
+ "5. A small cyan rubber cylinder rotated 29.3°. Fifth.\n",
+ "6. A large gray metal cube rotated 233.7°. Sixth.\n",
+ "7. A small yellow rubber cube rotated 233.7°. Seventh.\n",
+ "8. A small blue rubber sphere rotated 233.7°. Eighth.\n",
+ "9. A large purple rubber sphere rotated 233.7°. Ninth.\n",
+ "\n",
+ "Each of these entries describes a separate object.\n",
+ "predicted:None\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at the image provided.\n",
+ "\n",
+ "First, I need to count each object listed here. Each entry starts with \"A small cyan metal cylinder...\" or \"A large brown metal sphere...\" followed by their attributes like color, size, material, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Let's go through each line one by one:\n",
+ "\n",
+ "1. First line: A small cyan metal cylinder. That's one item.\n",
+ "2. Second line: A large blue rubber sphere. That's another item.\n",
+ "3. Third line: A large red metal sphere. Third item.\n",
+ "4. Fourth line: A large brown metal sphere. Fourth item.\n",
+ "\n",
+ "Each of these entries has distinct properties and coordinates, so they're separate objects. There are no mentions of any other items. So adding them up: 1 + 1 + 1 + 1 = 4. Therefore, there should be four items in total.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 4 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many gray cubes there are in the described scene. Let me go through each object one by one to check their colors and shapes.\n",
+ "\n",
+ "First, I need to look at each item listed and compare them against the criteria of being a \"gray cube\". \n",
+ "\n",
+ "Starting with the first object: a small brown rubber cylinder. The color here is brown, so that doesn't match. So this isn't a gray cube.\n",
+ "\n",
+ "Second object: a large red metal sphere. The color is red, so not gray. Shape is a sphere, so even if it were a cube, the color would be irrelevant. So no.\n",
+ "\n",
+ "Third object: a small yellow rubber sphere. Yellow, so not gray. Cube shape, but color mismatch.\n",
+ "\n",
+ "Fourth: a large green metal cylinder. Green, so not gray. Cube shape again, but color doesn't match.\n",
+ "\n",
+ "Fifth: a small brown rubber cube. This one has a brown color, which is different from gray. Even though the shape is a cube, the color is brown, so no.\n",
+ "\n",
+ "Sixth: a large red metal sphere. Red, so not gray. Cube shape, but color doesn't match.\n",
+ "\n",
+ "Seventh: a small purple rubber cylinder. Purple, so\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's tackle this question step by step. The user is asking if the number of shiny cubes to the right of the red shiny cylinder is greater than the number of matte things in front of the gray ball.\n",
+ "\n",
+ "First, I need to parse through each object in the scene and categorize them based on their properties. Let me list all the objects first:\n",
+ "\n",
+ "1. A small purple metal sphere rotated 235.6° located at 3D coordinates (0.78, -2.49, 0.35) and pixel coordinates (193, 132, 12.02).\n",
+ "2. A large blue metal cube rotated 135.3° at (-2.90, 2.60, 0.70) with pixel (222, 107, 10.77).\n",
+ "3. A small brown metal cube rotated 132.3° at (1.70, -1.70, 0.35) pixel (131, 132, 12.02).\n",
+ "4. A small red metal cylinder rotated 233.2° at\n",
+ "predicted:None\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I need to count each object mentioned in the list. Each entry starts with \"A small gray rubber cube...\" or similar. Let me go through each line:\n",
+ "\n",
+ "1. A small cyan metal sphere: That's one item.\n",
+ "2. A large green rubber sphere: Second item.\n",
+ "3. A small gray rubber cube: Third.\n",
+ "4. A small red rubber cylinder: Fourth.\n",
+ "5. A large purple rubber sphere: Fifth.\n",
+ "6. A small gray metal cube: Sixth.\n",
+ "7. A small blue rubber cube: Seventh.\n",
+ "\n",
+ "Each of these entries is a separate object. There are seven distinct items listed here. So the total number should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many yellow objects are either in front of the blue shiny block or large yellow cubes. Let me look at each object one by one.\n",
+ "\n",
+ "First, I need to identify all the objects mentioned in the scene. There are three objects described here: two cubes and one sphere. \n",
+ "\n",
+ "The first object is a large yellow metal cube. Its coordinates are (1.52, 0.73, 0.48) and rotation 169.2°. The second is a small yellow metal sphere with coordinates (0.70, -1.99, 0.35) and rotation 15.3°. The third is a large blue metal cube located at (-2.50, 2.83, 0.75) rotated 12.2°.\n",
+ "\n",
+ "Now, the key part of the question is to find objects that are either \"in front of\" the blue shiny block or large yellow cubes. First, I need to determine which objects are in front of the blue shiny block. The blue shiny block is the third object, so its coordinates are (-2.50, \n",
+ "predicted:None\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry in the image. Each entry starts with \"A small gray metal cube...\" followed by its attributes like color, size, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Starting with the first item: A small gray metal cube rotated 162.0° located at certain coordinates. That's one item. Next, another small gray metal cube, different coordinates. That's two items. Then a large blue rubber cube. Third is a small purple metal sphere. Fourth is a small brown metal cube. Fifth is a small blue rubber sphere. Sixth is a large green rubber cylinder. So that's six items in total. Each of these entries describes a distinct object with unique properties. There are no duplicates mentioned here. Therefore, the answer should be six.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each entry one by one.\n",
+ "\n",
+ "First line: \"A small gray metal sphere...\" That's one item. So that's the first object. Next line: \"A large brown metal cube...\" That's the second. Third line: \"A small blue metal cube...\" Third item. Fourth line: \"A large blue rubber cube...\" Fourth item. Fifth line: \"A small gray rubber cube...\" Fifth item. Sixth line: \"A small green metal cylinder...\" Sixth item. \n",
+ "\n",
+ "So each of these entries describes a separate object with different attributes like color, size, material, rotation, coordinates, and pixel coordinates. Each has their own distinct properties. Therefore, each is a separate item. There are six items listed here. I don't see any duplicates or groups that would reduce the count. So the total number should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 6 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many things are spheres to the left of the tiny yellow matte object or small spheres that are on the left side of the big blue thing.\n",
+ "\n",
+ "First, I need to parse through each item in the scene and figure out which ones fit into either category. Let me list them out:\n",
+ "\n",
+ "1. A large green metal cylinder...\n",
+ "2. A small cyan rubber sphere...\n",
+ "3. A large red rubber sphere...\n",
+ "4. A small brown rubber sphere...\n",
+ "5. A small cyan metal sphere...\n",
+ "6. A small brown rubber cube...\n",
+ "7. A large blue metal cube...\n",
+ "\n",
+ "Now, the first part: \"spheres to the left of the tiny yellow matte object\". So, we need to find all spheres that are to the left of the tiny yellow matte object. Let's check each sphere:\n",
+ "\n",
+ "- The first sphere (cyan) is at (-2.08, 0.90, 0.35). The second sphere (red) is at (-1.50, -2.80, 0.70). The third sphere (brown) is at (-1.50, 1.70, 0.70). The fourth sphere (cyan)\n",
+ "predicted:None\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I need to check each entry in the image provided. Each entry starts with \"A small cyan rubber cylinder...\" followed by its attributes like color, rotation, size, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Looking at each item:\n",
+ "\n",
+ "1. A small cyan rubber cylinder: This is one item. It's a cylinder with specific properties. So that's one.\n",
+ "2. A large blue metal cube: Another item. Different shape, color, and material. That's two items now.\n",
+ "3. A small red rubber sphere: Third item. Different shape again. So three items total.\n",
+ "4. A small purple rubber cylinder: Fourth item. Different color and material. Four items.\n",
+ "\n",
+ "Each of these entries has distinct characteristics and coordinates, so they're separate objects. There are no mentions of any other items. Therefore, the total number should be four.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other cubes are the same size as the rubber block. First, I need to identify all the objects in the scene that are cubes and compare their sizes to the rubber block.\n",
+ "\n",
+ "Looking through each object listed:\n",
+ "\n",
+ "1. A large blue metal cube: This is a cube, but its color is blue and material is metal. Its size is large. The question is about rubber blocks, so this one doesn't count because it's not made of rubber.\n",
+ "\n",
+ "2. A small gray rubber sphere: This is a sphere, so not a cube. So no.\n",
+ "\n",
+ "3. A large cyan rubber cylinder: Cylinder, so not a cube. So no.\n",
+ "\n",
+ "4. A small red rubber cylinder: Cylinder again, so not a cube. No.\n",
+ "\n",
+ "5. A small green rubber sphere: Sphere, so not a cube. Not counted.\n",
+ "\n",
+ "6. A large gray metal sphere: Sphere, so not a cube. No.\n",
+ "\n",
+ "7. A small gray rubber sphere: Another sphere, so not a cube. No.\n",
+ "\n",
+ "8. A small yellow metal sphere: Sphere, so not a cube. No.\n",
+ "\n",
+ "9. A large blue rubber cube: This is the rubber block mentioned in the question. The question asks for \"other\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many objects meet two conditions: being \"behind\" the large cyan rubber cylinder and located to the left of the small brown metal sphere.\n",
+ "\n",
+ "First, I need to identify all the objects in the scene. Let's list them out:\n",
+ "\n",
+ "1. A small cyan rubber cylinder rotated 235.0° at (-1.76, -0.49, 0.38) with pixel coordinates (191, 106, 0.35)\n",
+ "2. A large green metal cylinder rotated 235.0° at (1.90, -2.30, 0.75) with pixel coordinates (272, 103, 0.35)\n",
+ "3. A small brown metal sphere rotated 162.2° at (2.80, 0.70, 0.35) with pixel coordinates (300, 107, 0.35)\n",
+ "\n",
+ "Now, the first condition is that the object must be \"behind\" the large cyan rubber cylinder. To determine which objects are behind, we\n",
+ "predicted:None\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many gray objects are either big spheres or tiny metal cylinders. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to check each item in the scene for their properties. The user provided three objects: a large red rubber sphere, a small green metal cylinder, a large green metal cylinder, and a large blue metal cube.\n",
+ "\n",
+ "Looking at the first object: \"A large red rubber sphere\". The color here is red, which doesn't match the gray we're looking for. So this one doesn't count.\n",
+ "\n",
+ "Second object: \"small green metal cylinder\". The color is green, so that's not gray. Also, size is small, so it's not big. So this one also doesn't qualify.\n",
+ "\n",
+ "Third object: \"large green metal cylinder\". The color is green again, so no. Size is large, but the color isn't gray. So this one is excluded as well.\n",
+ "\n",
+ "Fourth object: \"large blue metal cube\". The color is blue, which is different from gray. Size is large, but the material is metal, but the shape is a cube, not a cylinder. So this one doesn't fit either.\n",
+ "\n",
+ "Now, since none of the objects have the color gray\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many balls are on the right side of the large red ball. First, I need to figure out which object is the \"large red ball\" in the scene.\n",
+ "\n",
+ "Looking at the image, there are five objects listed. Let me go through each one:\n",
+ "\n",
+ "1. A small yellow metal sphere: This is a sphere, but its color is yellow, so not red. So this isn't the one we're looking for.\n",
+ "2. A large cyan metal sphere: This is a sphere, but its color is cyan, so not red. So this is also not the target.\n",
+ "3. A large red rubber sphere: This is a sphere with red color and large size. Wait, the question says \"balls\", but the material here is rubber. But since the question uses \"balls\", maybe they mean spheres regardless of material. So this might be the one we're interested in.\n",
+ "4. A small yellow metal sphere: Again, not red or large. So no.\n",
+ "5. A large purple rubber cylinder: Cylinder, not a ball. So this is irrelevant.\n",
+ "\n",
+ "So the third object is the large red rubber sphere. Now, the question is about balls (spheres) that are to the right of\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking for the number of large metal objects that share the same color as the rubber cube.\n",
+ "\n",
+ "First, I need to identify all the objects in the scene and their properties. Let me list them out:\n",
+ "\n",
+ "1. A small purple rubber cylinder rotated 235.4° located at certain coordinates.\n",
+ "2. A large gray metal sphere at specific coordinates.\n",
+ "3. A small yellow metal cube with rotation 167.0° and coordinates.\n",
+ "4. A small blue rubber sphere with rotation 198.2° and coordinates.\n",
+ "5. A large cyan rubber sphere with rotation 10.7° and coordinates.\n",
+ "6. A small red metal cylinder with rotation 132.2° and coordinates.\n",
+ "7. A small purple metal sphere with rotation 233.5° and coordinates.\n",
+ "\n",
+ "Now, the key here is to find which of these objects are large and made of metal, and then check if they match the color of the rubber cube. The rubber cube is described as \"small blue rubber cube\". So we're looking for a large metal object that is also blue.\n",
+ "\n",
+ "Looking through each object:\n",
+ "\n",
+ "- The first one is a small cylinder,\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at each item listed one by one.\n",
+ "\n",
+ "First, I need to count all the objects mentioned. Each entry starts with \"A large cyan metal sphere...\" followed by its attributes like color, size, rotation, coordinates, and pixel coordinates. Each of these entries seems to be a separate object. \n",
+ "\n",
+ "Looking through all seven entries:\n",
+ "\n",
+ "1. A large cyan metal sphere...\n",
+ "2. A small brown metal cube...\n",
+ "3. A small blue rubber cube...\n",
+ "4. A large yellow metal cube...\n",
+ "5. A small gray rubber cube...\n",
+ "6. A large red metal cylinder...\n",
+ "7. A small cyan metal sphere...\n",
+ "\n",
+ "Each of these are distinct objects. There are no mentions of any duplicates or groups that would combine into a single item. So each entry is a separate entity. Therefore, counting them individually gives us seven items.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many shiny cylinders are in front of the big yellow rubber sphere. Let me look at the scene described.\n",
+ "\n",
+ "First, I need to identify all the objects mentioned. There are three objects listed here: two cylinders and one sphere. The first cylinder is large, cyan, made of metal, and located at certain coordinates. The second cylinder is also large, cyan, made of metal, and positioned differently. The third object is a large yellow rubber sphere.\n",
+ "\n",
+ "Now, the key part is to determine which of these cylinders are \"shiny\" and whether they are \"in front of\" the yellow sphere. Let's break down each condition:\n",
+ "\n",
+ "1. **Shiny cylinders**: The material of the cylinders is specified as \"metal\" or \"rubber\". Since the question mentions \"shiny\", we need to check if any of the cylinders are made of metal. The first and second cylinders are both made of metal, so they are shiny. The third object is rubber, so not shiny.\n",
+ "\n",
+ "2. **Behind the yellow sphere**: The question says \"behind the yellow thing\". So, we need to determine the position of each cylinder relative to the yellow sphere. The yellow\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each item mentioned in the image. Each entry starts with \"A large brown rubber cube...\" followed by its attributes. Then there's another line for a large cyan rubber cylinder, then another for a small green metal cylinder, and finally a large gray metal cube. \n",
+ "\n",
+ "Each of these entries describes a separate object. There are four distinct objects here: a cube, a cylinder, another cylinder, and another cylinder. So that's four items total. Even though two of them are cylinders, they're different objects. So adding them together should give us the total number of items. Therefore, the answer should be 4.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 6 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other rubber cylinders have the same color as the one mentioned in the scene. First, I need to identify all the objects that are rubber cylinders.\n",
+ "\n",
+ "Looking at the image, there are four objects listed. Let me check each one:\n",
+ "\n",
+ "1. A small red rubber cylinder. This is a rubber cylinder with red color. So this is one rubber cylinder.\n",
+ "2. A large green metal cylinder. This is a metal cylinder, so not relevant here.\n",
+ "3. A large brown metal cube. Metal cube, no.\n",
+ "4. A large blue rubber sphere. Rubber sphere, but blue color.\n",
+ "\n",
+ "So the only rubber cylinder mentioned is the first one: small red rubber cylinder. Now, the question is asking for other rubber cylinders with the same color (red). Since there's only one rubber cylinder, and its color is red, there can't be any others with the same color. Therefore, the answer should be zero.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of cyan objects in front of the small blue metallic cube is the same as the number of big green spheres. So first, I need to figure out how many cyan objects are in front of the small blue metallic cube. Then, I need to count the big green spheres. Let me go through each object one by one.\n",
+ "\n",
+ "Starting with the cyan objects. Looking at the list, there's a \"small cyan metal cylinder\" which is a cylinder. Since cylinders aren't typically considered spheres or cubes, this might not be relevant. But maybe there's another cyan object? Let me check all the cyan objects again. There's no other cyan object listed here. So the answer for cyan objects would be zero.\n",
+ "\n",
+ "Now, the small blue metallic cube. The question says \"in front of\". In 3D space, the front plane is usually defined as the plane that's parallel to the viewer's line of sight. However, since the image doesn't specify any coordinate system or camera position, we can assume the default viewing direction. But even without that, the coordinates might help. For example, if the camera is looking along the positive z-axis (forward), then objects closer to the camera would\n",
+ "predicted:None\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many large balls are in front of the small green metal sphere. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to identify all the objects that are large balls. The scene lists several objects: a small red rubber cylinder, a large blue metal sphere, a small green metal sphere, a large red rubber sphere, a small gray rubber cube, another small green metal cylinder, and a large green rubber sphere. \n",
+ "\n",
+ "Looking for \"large balls\" here. The large balls mentioned are the large red rubber sphere and the large blue metal sphere. So there are two large balls in total.\n",
+ "\n",
+ "Next, we need to determine which of these are in front of the small green metal sphere. The small green metal sphere is located at coordinates (-1.23, 0.75, 0.48) and rotated 296.3°. The large balls are the red rubber sphere (at -0.32, 1.90, 0.70, rotated 19.0°) and the blue metal sphere (2.70, -1.90, 0.70\n",
+ "predicted:None\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of tiny blue metal cylinders is the same as the number of big blue metal things in the described scene.\n",
+ "\n",
+ "First, I need to look through each object listed and check both categories: size (tiny vs. big) and material (metal vs. other). Let me go through each one:\n",
+ "\n",
+ "1. **Large gray metal cube**: Size is large, material is metal. Not relevant here.\n",
+ "2. **Small cyan rubber cylinder**: Small, color cyan, material rubber. Doesn't match either category.\n",
+ "3. **Large green rubber cylinder**: Large, green, rubber. Again, not relevant.\n",
+ "4. **Large blue rubber cube**: Large, blue, rubber. So this is a big blue thing but not metal. So that's 1.\n",
+ "5. **Large yellow metal sphere**: Large, yellow, metal. That's another big blue metal thing. So that's 2.\n",
+ "6. **Large cyan metal sphere**: Large, cyan, metal. That's a big blue metal thing again. So that's 3.\n",
+ "7. **Large blue metal cube**: Large, blue, metal. This is a big blue metal thing. So that's 4.\n",
+ "\n",
+ "Now, counting the tiny\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of blue matte cubes is less than the number of big green cylinders. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to count the number of blue matte cubes. Looking at the scene, there's a small brown rubber cube. So that's 1 blue matte cube.\n",
+ "\n",
+ "Next, the question mentions \"big green cylinders\". In the image, there's a large green metal cylinder. Since the material here is metal (metal), not rubber, so this doesn given in the question. But the size is large. So this counts as a green cylinder. Wait, but the question says \"big green cylinders\", which would be a large green cylinder. The cylinder here is large, so it fits. Therefore, that's another one.\n",
+ "\n",
+ "Now, comparing these two: 1 blue matte cube vs. 1 large green cylinder. So the number of blue matte cubes is 1, and the number of large green cylinders is also 1. They are equal. Therefore, the answer should be no, because 1 is not less than 1.\n",
+ " no \n",
+ "predicted:no\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let's see. The user provided a scene with three objects. Let me check each item one by one.\n",
+ "\n",
+ "First object: A small yellow metal sphere rotated 162.3° located at certain coordinates. The second is a large green metal cylinder, also at specific coordinates. The third is a small yellow rubber cube. Each of these objects has their own distinct properties like color, material, size, rotation, and coordinates. Since each entry is a separate object with different attributes, they should be counted as individual items. Therefore, the total number of items here should be three.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 4 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I need to count all the objects mentioned in the scene. Each entry starts with \"A large green metal cylinder...\" or \"A small gray rubber cube...\" etc. \n",
+ "\n",
+ "Looking through each line:\n",
+ "\n",
+ "1. A large purple metal cylinder: That's one item.\n",
+ "2. A small red rubber cylinder: Second item.\n",
+ "3. A large purple metal cylinder: Third item.\n",
+ "4. A small gray rubber cube: Fourth.\n",
+ "5. A large gray rubber cube: Fifth.\n",
+ "6. A small blue metal cube: Sixth.\n",
+ "7. A large green metal cylinder: Seventh.\n",
+ "8. A large purple metal cylinder: Eighth.\n",
+ "\n",
+ "Each of these entries describes a distinct object. There are eight separate entries here. So that's eight items in total.\n",
+ " 8 \n",
+ "predicted:8\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are the same number of rubber spheres and yellow shiny blocks in the scene. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to look at each item listed and determine if they are both either rubber spheres or both yellow shiny blocks.\n",
+ "\n",
+ "Looking at the first object: A large cyan rubber sphere. Since it's a sphere (shape) and made of rubber (material), that's a rubber sphere. So this counts as 1.\n",
+ "\n",
+ "Next, the second object is a small cyan metal cylinder. Cylinder isn given here, so not a sphere. Also, the material is metal, which is not rubber. So this doesn't count towards the count.\n",
+ "\n",
+ "Third is a large purple metal cylinder. Cylinder again, so no. Material is metal, so not a rubber sphere. So this also doesn't count.\n",
+ "\n",
+ "Fourth is a small gray metal cube. Cube, but the color is gray, not yellow. So this is a different type of object.\n",
+ "\n",
+ "Fifth is a large yellow rubber cube. This is a cube, but the color is yellow and it's made of rubber. So that's another rubber block. So that's the second rubber object.\n",
+ "\n",
+ "So now, comparing the two counts: \n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's tackle this question step by step. The user is asking for the number of tiny things that are either blocks or shiny objects in the described scene.\n",
+ "\n",
+ "First, I need to parse the image provided. There are three objects listed here:\n",
+ "\n",
+ "1. A large brown metal sphere rotated 235.0° located at certain coordinates.\n",
+ "2. A small gray metal sphere at different coordinates.\n",
+ "3. A small blue rubber cube with specific coordinates.\n",
+ "\n",
+ "The question is about counting how many of these objects are either blocks (small cubes) or shiny (metallic). Let's break down each object one by one.\n",
+ "\n",
+ "Looking at the first object: It's a large metal sphere. Since it's a sphere, not a block, so it doesn't qualify as a block. Its color is brown and material is metal, which makes it shiny. So this counts as a shiny object. Therefore, it's counted once.\n",
+ "\n",
+ "Second object: Small gray metal sphere. This is a sphere again, so not a block. But since it's small and made of metal, it's also a shiny object. So that's another count.\n",
+ "\n",
+ "Third object: Small blue rubber cube. This is a cube, which is a block. So it's both a block and\n",
+ "predicted:None\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of small gray matte cylinders is less than the number of large balls. First, I need to parse through each object in the scene provided.\n",
+ "\n",
+ "Looking at the list:\n",
+ "\n",
+ "1. A large yellow rubber cube rotated 230.7° located at certain coordinates.\n",
+ "2. A large blue rubber sphere rotated 196.5° at different coordinates.\n",
+ "3. A small gray rubber sphere rotated 148.2° with specific coordinates.\n",
+ "4. A small cyan rubber cylinder rotated 162.3° with coordinates.\n",
+ "5. A small green metal cylinder rotated 232.2°.\n",
+ "6. A large gray rubber cube rotated 233.3°.\n",
+ "\n",
+ "Now, let's break down the two parts of the question: \"small gray matte cylinders\" and \"large balls\".\n",
+ "\n",
+ "First, identifying the small gray matte cylinders. Looking through the objects, there's only one small gray rubber sphere (object 3), which doesn't match the description of a cylinder. So that's zero.\n",
+ "\n",
+ "Next, for large balls. The large balls here refer to the large blue rubber sphere (object 2). There's only one such ball. \n",
+ "\n",
+ "Comparing these\n",
+ "predicted:None\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are the same number of brown metallic blocks left of the cube and cyan matte cylinders that are behind the big cylinder.\n",
+ "\n",
+ "First, I need to parse through each object in the scene. Let me list them out:\n",
+ "\n",
+ "1. A large purple rubber cylinder (size: large, color: purple, material: rubber)\n",
+ "2. A small cyan rubber sphere (size: small, color: cyan, material: rubber)\n",
+ "3. A small cyan rubber cylinder (size: small, color: cyan, material: rubber)\n",
+ "4. A small brown metal cube (size: small, color: brown, material: metal)\n",
+ "5. A small purple metal cylinder (size: small, color: purple, material: metal)\n",
+ "\n",
+ "Now, the first part of the question is \"Are there the same number of brown metallic blocks left of the cube?\" \n",
+ "\n",
+ "Looking at the objects, the cube is the fourth one listed. So we need to count how many brown metallic blocks are to the left of this cube. Let's check each block:\n",
+ "\n",
+ "- The first object is a cylinder, so no.\n",
+ "- Second object is a sphere, also no.\n",
+ "- Third object is another cylinder, still no.\n",
+ "- Fourth object is a cube\n",
+ "predicted:None\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small brown rubber cube...\" or similar. Let me count them all.\n",
+ "\n",
+ "1. A large brown rubber sphere: That's one item.\n",
+ "2. A small red rubber cylinder: Second item.\n",
+ "3. A small purple rubber sphere: Third.\n",
+ "4. A small blue rubber cube: Fourth.\n",
+ "5. A small red rubber sphere: Fifth.\n",
+ "6. A small brown rubber sphere: Sixth.\n",
+ "7. A large red rubber sphere: Seventh.\n",
+ "8. A small red rubber cylinder: Eighth.\n",
+ "\n",
+ "Each of these entries describes a separate object. There are eight distinct items listed here. So the total number should be 8.\n",
+ " 8 \n",
+ "predicted:8\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at each object listed one by one.\n",
+ "\n",
+ "First, I'll check the first line: \"A large cyan metal sphere...\" That's one item. Next, the second line says \"A small purple rubber sphere...\". That's another item. The third line mentions a \"small brown metal cube\". So that's the third item. The fourth line starts with \"A large purple rubber sphere\", which is the fourth item. Each of these entries has distinct attributes like color, size, material, shape, rotation, 3D coordinates, and pixel coordinates. Since each entry is a separate object with different properties, they are all individual items. Therefore, there should be four items in total.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 4 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at each object listed one by one.\n",
+ "\n",
+ "First, I'll check each entry to count them. Each entry starts with \"A small yellow metal sphere...\" followed by its attributes. There's another entry for a large gray rubber cube, then a small purple rubber sphere, and finally a large red metal cylinder. That's four distinct objects. Each of these entries has unique properties like color, material, shape, rotation, size, coordinates, and pixel coordinates. Since each is a separate item with different characteristics, they should all be counted as individual items. So total of 4 items.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many objects are either \"blocks\" or \"gray shiny\". Let me look at each object one by one.\n",
+ "\n",
+ "First, I need to check each item in the scene for both categories: \"blocks\" and \"gray shiny\".\n",
+ "\n",
+ "Looking at the first object: A large purple metal cylinder. It's a cylinder, not a cube (which would be a block), and its color is purple. So this doesn given category is \"cylinder\", so it doesn't count here.\n",
+ "\n",
+ "Second object: a small red metal sphere. This is a sphere, which isn't a block. Its color is red, so it's not gray or shiny. Therefore, it also doesn't count.\n",
+ "\n",
+ "Third object: a large gray metal cube. This is a cube, which is a type of block, and it's gray and shiny. So this counts as a block. \n",
+ "\n",
+ "So that's two items that fit the criteria: 2.\n",
+ " 2 \n",
+ "predicted:2\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same shape as the \"tiny rubber object\" in the scene.\n",
+ "\n",
+ "First, I need to identify which object is described as \"tiny rubber\". Looking through the list:\n",
+ "\n",
+ "The first line mentions a \"small gray metal cube\". That's not rubber. Next, a \"large cyan rubber cylinder\". This one is rubber, but large. Then, a \"small cyan metal cylinder\". Metal, so not rubber. The next is a \"small blue metal cube\", again metal. The last is a \"large green metal cylinder\". Metal again. \n",
+ "\n",
+ "So the only rubber object here is the \"large cyan rubber cylinder\". Now, we need to count how many other objects have the same shape (cylinder) as this rubber cylinder.\n",
+ "\n",
+ "Looking at all the cylinders listed: the small cyan metal cylinder, the large green metal cylinder, and the large cyan rubber cylinder itself. So that's three cylinders total. But the question asks for \"other objects\", excluding the rubber cylinder itself. Therefore, the answer should be 2.\n",
+ " 2 \n",
+ "predicted:2\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each entry one by one.\n",
+ "\n",
+ "First line: \"A large green rubber cylinder...\" That's one item. Next, another \"large green rubber cylinder\" which is the same as the first one, so that's two. Third line: \"A small yellow rubber sphere\". Third item. Fourth: \"A small red rubber sphere\". Fourth item. Fifth: \"A large purple rubber sphere\". Fifth item. Sixth: \"A small green metal cylinder\". Sixth item. So each of these entries is a separate object. There are six distinct objects listed here. Each has different attributes like color, size, material, shape, rotation, coordinates, and pixel coordinates. Therefore, the total number should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 6 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry in the image. Each entry starts with \"A small cyan metal cylinder...\" or \"A large yellow rubber cube...\" followed by their attributes like color, size, shape, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Starting with the first item: A small cyan metal cylinder. That's one item. Next, a large gray rubber sphere. Second item. Then a small red rubber cylinder. Third is a large blue rubber sphere. Fourth is a small gray rubber sphere. Fifth is a large yellow rubber cube. Sixth is a small cyan rubber cylinder. Seventh is a large red rubber cylinder. So that's seven items total. Each of these entries describes a distinct object with different properties. There are no duplicates mentioned here. Therefore, the answer should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many green matte balls are the same size as the gray metallic cube. Let me look at each object one by one.\n",
+ "\n",
+ "First, I need to identify all objects in the scene that are green and made of metal. The first object is a large brown metal cube. Its color is brown, so it doesn't match the green color we're looking for. Next, there's another cube, which is gray and made of metal. The material here is metal, but its color is gray, not green. So this one also doesn't fit.\n",
+ "\n",
+ "Now, the third object is a gray rubber cube. This one is gray and made of rubber, so it's not even green or metal. Therefore, none of the cubes listed have both being green and made of metal. Since there are no other objects mentioned, the answer should be zero.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many large objects are either yellow cubes or green metal cylinders. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to check each item in the scene for both categories: yellow cubes and green metal cylinders. \n",
+ "\n",
+ "Looking at the first object: A small yellow rubber cube. Since it's a cube and not a cylinder, it doesn given as a cube, so that's a yellow cube. So this counts towards the count. Next, the next object is a large brown rubber sphere. This is a sphere, so it's not a cube or cylinder. So no contribution here. The third object is a small cyan rubber cylinder. Cylinder but not yellow or green. Fourth is a large green metal cylinder. That's a green metal cylinder. So that's one. The fifth is a small yellow rubber sphere. Sphere again, so no. The sixth is a small purple rubber cylinder. Cylinder, but not green or yellow. The seventh is a large green rubber sphere. Again, sphere, so no. The eighth is a small yellow rubber sphere. Sphere, so no. The ninth is a large red rubber cylinder. Cylinder, but not green or yellow. The tenth is a small green metal\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many balls are left of the small blue object. First, I need to figure out which objects are balls and which one is the small blue object.\n",
+ "\n",
+ "Looking at the scene, there are five objects listed. Let me go through each one:\n",
+ "\n",
+ "1. A large gray rubber cube rotated 235.6° with coordinates (0.78, -2.49, 0.7) and pixel coordinates (220, 162, 12.3). This is a cube, so not a ball.\n",
+ "2. A small yellow metal cube at (-2.13, 1.79, 0.3) – also a cube.\n",
+ "3. A small green rubber sphere at (1.76, -1.90, 0.3) – this is a sphere. So that's one ball.\n",
+ "4. A large gray metal cube at (-2.83, 0.33, 0.7) – another cube.\n",
+ "5. A small red metal cylinder at (1.99, -1.70, 0.3) – cylinder.\n",
+ "\n",
+ "Now, the question mentions \"small\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A large gray metal cube...\" followed by its attributes. Then there's a small purple rubber sphere, then another large red metal sphere, a large gray rubber sphere, and a large purple metal cylinder. \n",
+ "\n",
+ "Each of these entries seems to be separate objects. There are four distinct items here: a cube, two spheres (one purple rubber and one red metal), and a cylinder. So adding them up: 1 + 2 + 2 = 5. Therefore, there should be five items in total.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry in the image. Each entry starts with \"A small blue rubber cube...\" or \"A large gray metal cube...\" followed by their coordinates and rotation. \n",
+ "\n",
+ "The first line mentions a small blue rubber cube. That's one item. Next, another small blue rubber cube. That's two items. Then a large gray metal cube. Third is a small red rubber cylinder. Fourth is a large gray rubber cube again. And lastly, a large blue rubber cube. So that's five items total. Each of these entries describes a distinct object with different attributes like color, size, material, shape, rotation, and coordinates. Since each entry is separate and distinct, they're all individual objects. Therefore, the answer should be 5.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are the same number of blue rubber balls in front of the cylinder and big matte spheres that are in front of the large gray metal cube.\n",
+ "\n",
+ "First, I need to parse the scene described. There are three objects: a large gray metal cube, a small cyan rubber cylinder, and a large blue rubber sphere. Let's break down each part of the question step by step.\n",
+ "\n",
+ "1. **Blue rubber balls**: Are there any blue rubber balls in front of the cylinder?\n",
+ " - First, identify all blue rubber objects. The only blue object mentioned is the \"large blue rubber sphere\". So there's one blue rubber ball here.\n",
+ "\n",
+ "2. **Big matte spheres**: Are there any big matte spheres in front of the cylinder?\n",
+ " - The cylinder is the small cyan rubber cylinder. Now, check all other objects for big matte spheres. The large blue rubber sphere is a sphere, but its material is rubber, not matte. The other objects are either small or large, but their materials are different (metal and metal). So there are no big matte spheres in front of the cylinder.\n",
+ "\n",
+ "3. Compare the two counts:\n",
+ " - Blue rubber balls: 1\n",
+ " - Big matte spheres: 0\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The user provided a scene with several objects. I need to count them one by one.\n",
+ "\n",
+ "First, I'll go through each item listed here. Let me check each entry carefully.\n",
+ "\n",
+ "The first object is a large cyan rubber cylinder. That's one item. Next, another large purple rubber cylinder. That's two items. Then a small brown metal cube. Third is a small purple rubber cylinder. Fourth is another large cyan metal cylinder. And lastly, another large purple rubber cylinder. So that's five items in total. Each of these entries describes a separate object with different attributes like color, size, material, shape, rotation, 3D coordinates, and pixel coordinates. Since each has distinct properties, they're all individual items. Therefore, the answer should be five.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 6 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of other big purple cylinders that share the same material as the given object. First, I need to identify the objects in the scene that are both large, purple, and made of metal.\n",
+ "\n",
+ "Looking through each item one by one:\n",
+ "\n",
+ "1. The first object is a large gray rubber cube. It's not purple or a cylinder, so it doesn't qualify.\n",
+ "2. The second is a small purple metal sphere. This one is purple but a sphere, so it doesn't match.\n",
+ "3. The third is a large green metal sphere. Green, so no.\n",
+ "4. The fourth is a small purple metal sphere. Same color and material as the original, but size is different.\n",
+ "5. The fifth is a large purple metal sphere. This one is also a sphere, so it's not a cylinder.\n",
+ "6. The sixth is a large gray rubber cube. Again, not a cylinder.\n",
+ "\n",
+ "So, none of the other objects are cylinders. Therefore, there are zero other big purple cylinders with the same material as the given object.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many cylinders are either large metallic objects or large objects in the scene. Let me go through each item one by one.\n",
+ "\n",
+ "First, I need to identify all the cylinders in the image. Looking at the list:\n",
+ "\n",
+ "1. A small green rubber cylinder: This is a cylinder but not large or metallic. So that's one.\n",
+ "2. A large green metal cylinder: Yes, this is a cylinder and it's large and metallic. That's two.\n",
+ "3. A large green rubber cylinder: Another cylinder, but it's rubber, so not counted here.\n",
+ "4. A large green metal cylinder: Same as the second one, same attributes.\n",
+ "5. A large yellow rubber cube: Cube, not a cylinder. So no.\n",
+ "6. A large brown metal sphere: Sphere, not a cylinder. So no.\n",
+ "7. A small cyan rubber sphere: Sphere, not a cylinder. So no.\n",
+ "8. A large green rubber cylinder: Again, rubber, so not counted.\n",
+ "9. A large brown metal sphere: Sphere again, not a cylinder. So no.\n",
+ "\n",
+ "So the cylinders that are either large and metallic (metallic) or large (regardless of material) are the first two and the third.\n",
+ "predicted:None\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are the same number of large red shiny cylinders as there are small blue rubber cubes. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to look at each item in the scene and determine if they meet either being a \"large red shiny cylinder\" or a \"small blue rubber cube\". \n",
+ "\n",
+ "Looking at the first object: A large cyan metal sphere. That's a sphere, not a cylinder, so that doesn given criteria. So this doesn given.\n",
+ "\n",
+ "Second object: A small purple metal sphere. Again, not a cylinder. So no.\n",
+ "\n",
+ "Third: Large green rubber cylinder. This is a cylinder but it's green, so not red. Also, it's large, so it's a large one. But the color is green, so it doesn't match the red color. So this is a non-match.\n",
+ "\n",
+ "Fourth: Small yellow metal cube. This is a cube, not a cylinder. So no.\n",
+ "\n",
+ "Fifth: Large red metal sphere. Sphere again, so not a cylinder. So no.\n",
+ "\n",
+ "Sixth: Small blue rubber cube. Cube, not a cylinder. So no.\n",
+ "\n",
+ "Seventh: Small gray metal sphere. Sphere, so not a cylinder. So no.\n",
+ "\n",
+ "Eighth\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of tiny brown cylinders that are made of the same material as the large red cylinder. First, I need to look at each object in the scene provided.\n",
+ "\n",
+ "Looking at the first object: A small green rubber cylinder. Its color is green, so it doesn't match the red one. So this one doesn't count.\n",
+ "\n",
+ "Second object: A large yellow rubber sphere. Yellow isn't red, so no. So this one also doesn't fit.\n",
+ "\n",
+ "Third object: A small blue rubber cube. Blue again, not red. So this one is excluded.\n",
+ "\n",
+ "Fourth object: A large brown metal cube. This one is brown but made of metal, which is different from the red cylinder. Therefore, it's not relevant.\n",
+ "\n",
+ "Now, the key here is to find any other objects that are both small (small) and brown (color) and made of the same material as the large red cylinder. The red cylinder is described as \"large\" and \"rubber\". So we're looking for a small brown rubber object.\n",
+ "\n",
+ "Wait, let me check all objects again. The only small object is the blue cube. It's blue, so that's not brown. The other objects are either large or have different\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each entry one by one.\n",
+ "\n",
+ "First item: A large gray metal sphere. That's one object. Next, a small green rubber cylinder. Second item. Third is a small red rubber sphere. Fourth is a large purple metal cylinder. Fifth is another small green metal cylinder. Sixth is a small gray rubber cube. Seventh is a small cyan rubber cylinder. Eighth is a small red metal sphere. So that's eight items listed here. Each entry describes a separate object with different attributes like color, size, shape, material, rotation, coordinates, and pixel coordinates. Since each of these entries is a distinct object, the total number should be 8.\n",
+ " 8 \n",
+ "predicted:8\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small gray rubber cube...\" or \"A large yellow metal cube...\" followed by coordinates and rotation. \n",
+ "\n",
+ "1. First object: A small gray rubber cube. That's one item.\n",
+ "2. Second: A large red rubber sphere. That's two items.\n",
+ "3. Third: A large yellow metal cube. Third item.\n",
+ "4. Fourth: A small yellow rubber sphere. Fourth.\n",
+ "5. Fifth: A small red metal cylinder. Fifth.\n",
+ "\n",
+ "So, counting them all together: 1 + 2 + 3 + 4 + 5 = 11. So there should be five items in total.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of small rubber balls that meet two conditions: they are behind the brown metallic cube and in front of the tiny green metallic thing.\n",
+ "\n",
+ "First, I need to parse through each object in the scene. Let me list them all:\n",
+ "\n",
+ "1. A large gray rubber cube...\n",
+ "2. A small yellow rubber sphere...\n",
+ "3. A small cyan metal cylinder...\n",
+ "4. A small purple rubber cylinder...\n",
+ "5. A small brown metal cube...\n",
+ "6. A small green rubber cylinder...\n",
+ "7. A small green metal cylinder...\n",
+ "\n",
+ "Now, the first condition is \"behind the brown metallic cube\". So, I need to find all objects that are behind the fifth object (the brown cube). To determine which objects are behind another, we need to consider their coordinates. Typically, in 3D space, if an object is behind another, its depth value would be less than the other object's. However, since the scene doesn't specify depth values, I'll assume the camera is at the origin looking along the positive z-axis. Therefore, any object with a higher z-coordinate than the brown cube would be behind it. \n",
+ "\n",
+ "The brown cube has coordinates (0.89, -0.70, \n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many rubber objects share the same color as the small gray metal sphere. First, I need to identify all the objects in the scene and their properties.\n",
+ "\n",
+ "Looking at each object:\n",
+ "\n",
+ "1. A large blue rubber sphere: This one is a rubber object, but its color is blue, not gray. So this doesn't match.\n",
+ "2. A small brown rubber cube: This is another rubber object, but brown, not gray. So no match here.\n",
+ "3. A large purple metal cylinder: Metal, so not a rubber object. No.\n",
+ "4. A small gray metal sphere: This is the target object. It's a metal sphere, but the color is gray. Now, the question asks for rubber objects with the same color (gray). Since there are no other rubber objects mentioned, the answer should be zero.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many objects meet certain criteria: being small metallic things or metal cylinders that are to the right of a \"big metal thing.\" First, I need to parse through each object in the scene.\n",
+ "\n",
+ "Looking at the first object: a large red metal cylinder. That's the \"big metal thing\" mentioned. Now, I need to check all other objects to see if they fit either category.\n",
+ "\n",
+ "The second object is a small blue metal cube. Is this a small metallic thing? Yes, since it's small and made of metal. So that counts as one. Next, the third object is a small cyan metal cylinder. This is also a small metallic thing. So that's another one. \n",
+ "\n",
+ "Now, the question specifies \"metal cylinders that are right of the big metal thing.\" Since the big metal thing is the red cylinder, we need to determine which cylinders are to its right. Assuming the coordinate system where the x-axis points right, the red cylinder would be on the left side. Therefore, any cylinders to the right of the red cylinder should be cyan and small (since there's only one such cylinder). \n",
+ "\n",
+ "So, both the small blue cube and the small cyan cylinder are small metallic things. But the\n",
+ "predicted:None\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many yellow balls there are in the scene. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to look at each item listed and check if they are both yellow and a ball. \n",
+ "\n",
+ "Looking at the first object: A large cyan rubber cylinder. That's not a ball, so no.\n",
+ "\n",
+ "Second object: small green metal sphere. Green, so not yellow. So this doesn't count.\n",
+ "\n",
+ "Third: small yellow rubber sphere. This one is yellow and a sphere. So that's one.\n",
+ "\n",
+ "Fourth: large cyan rubber sphere. Cyan, so not yellow. No.\n",
+ "\n",
+ "Fifth: small purple metal cylinder. Purple, not yellow. Not a ball either.\n",
+ "\n",
+ "Sixth: small yellow metal cube. Yellow but a cube, so not a ball.\n",
+ "\n",
+ "Seventh: small cyan metal cylinder. Cyan again, not yellow.\n",
+ "\n",
+ "Eighth: small yellow metal cube. Same issue here; yellow but a cube.\n",
+ "\n",
+ "So adding up all the yellow balls: only one (the third one). Therefore, the answer should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of metallic objects that are either big red things or small cubes. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to identify all the objects in the scene and check their properties. There are seven objects listed here. Each has attributes like color, size, shape, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Starting with the first object: A large cyan metal sphere. It's a sphere, so its shape is not a cube. Its color is cyan, which isn't red. So this doesn't count towards the answer.\n",
+ "\n",
+ "Second object: A small brown metal cube. This is a cube, but it's small, so it's not counted. The color is brown, not red. So no.\n",
+ "\n",
+ "Third: Large purple metal cylinder. Cylinder, not a cube. Color is purple, so not red. Not counted.\n",
+ "\n",
+ "Fourth: Small green rubber cylinder. Cylinder again, not a cube. Green color, so not relevant.\n",
+ "\n",
+ "Fifth: Large red metal cylinder. This one is a cylinder, but the color is red. Since it's a large object, it counts as a \"big\" in this context. So this counts.\n",
+ "\n",
+ "Sixth: Small\n",
+ "predicted:None\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small purple rubber cylinder...\" or similar. Let me count them all.\n",
+ "\n",
+ "1. A large cyan metal sphere: That's one item.\n",
+ "2. A small blue rubber cube: Second item.\n",
+ "3. A large red rubber sphere: Third.\n",
+ "4. A small purple rubber cylinder: Fourth.\n",
+ "5. A small blue rubber cube: Fifth.\n",
+ "6. A large green rubber cylinder: Sixth.\n",
+ "7. A small red rubber cylinder: Seventh.\n",
+ "\n",
+ "So, counting these six entries, that's seven items total. There are no other objects mentioned. So the answer should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are an equal number of big blocks that are in front of the yellow thing and tiny blocks. So first, I need to figure out which objects are big blocks, small blocks, and the yellow thing itself.\n",
+ "\n",
+ "Looking at the scene:\n",
+ "\n",
+ "1. **Big blocks**: There's one large gray rubber cube. That's the only big block mentioned. So that's 1 big block.\n",
+ "\n",
+ "2. **Small blocks**: There are three small cubes: two brown ones and one gray. So total of 3 small blocks.\n",
+ "\n",
+ "Now, the yellow thing is the large yellow metal sphere. But the question is about \"in front of\" this yellow object. Wait, but the image doesn't mention any coordinates or positions. So how do we determine which objects are in front of the yellow sphere?\n",
+ "\n",
+ "Wait, the user might be referring to the camera's perspective. In 3D space, the camera would typically look along the negative z-axis (downwards) and rotate around the y-axis. However, without specific coordinates, it's hard to determine the exact position of each object relative to the camera. \n",
+ "\n",
+ "But since the question is about \"in front of,\" perhaps the camera is positioned such that the yellow\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A large green rubber cylinder...\" or \"A small purple rubber sphere...\" followed by their attributes like color, size, material, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Starting with the first item: A large green rubber cylinder. That's one item. Next, another large green rubber cylinder. That's two items. Then a small purple rubber sphere. Third is a large purple metal sphere. Fourth is a small purple rubber cylinder. Fifth is a large purple metal cylinder. Sixth is a small red metal sphere. Seventh is a large gray metal cube. Eighth is a large green rubber cylinder again. So that's seven items total. There are no duplicates mentioned here. Each entry is distinct objects with different properties. Therefore, the answer should be 8.\n",
+ " 8 \n",
+ "predicted:8\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of things that are either on the left side of the metallic sphere or green matte blocks. First, I need to figure out which objects are on the left side of the metallic sphere.\n",
+ "\n",
+ "Looking at the scene, there's a large yellow rubber sphere (the metallic sphere) located at 3D coordinates (-2.09, -1.78, 0.65). So, any object that is to the left of this sphere would have a negative x-coordinate relative to its position. Let me check each object:\n",
+ "\n",
+ "The first object is a small purple metal cylinder at (-2.40, -1.78, 0.32). Since its x-coordinate is -2.40, which is less than -2.09, it's to the left. So that counts as one.\n",
+ "\n",
+ "Next, the second object is a large brown rubber cube at (-1.90, -0.78, 0.75). Its x-coordinate is -1.90, which is also less than -2.09. So that's another one.\n",
+ "\n",
+ "Now, the third object is a large yellow rubber sphere at (-2.\n",
+ "predicted:None\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of metal objects that are either cyan cylinders or cyan balls. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to check each item in the scene to determine if they are both metal and either cyan cylinders or cyan balls. \n",
+ "\n",
+ "Starting with the first object: a small purple metal cylinder. The color here is purple, so it doesn't match the criteria. So this one doesn given.\n",
+ "\n",
+ "Second, a large brown rubber cube. Color is brown, not cyan, so no.\n",
+ "\n",
+ "Third, a small green metal cylinder. Green, so not cyan. Not relevant.\n",
+ "\n",
+ "Fourth, a small purple rubber cylinder. Purple again, so not cyan. Not counted.\n",
+ "\n",
+ "Fifth, a small blue metal sphere. Blue, so not cyan. Also not relevant.\n",
+ "\n",
+ "Sixth, a large cyan metal cylinder. Cyan color and a cylinder shape. This fits the criteria. So that's one.\n",
+ "\n",
+ "So adding up all the符合条件的 items: 1 + 0 + 0 + 0 + 0 = 1. Therefore, there's only one such object.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many purple metal objects are to the right of the thing that's right of the rubber cylinder behind the tiny red rubber object.\n",
+ "\n",
+ "First, I need to parse through each object in the scene. Let me list them out:\n",
+ "\n",
+ "1. A small yellow rubber sphere rotated 239.7° at 3D coordinates (0.58, -2.64, 0.35) with pixel coordinates (191, 101, 12.0).\n",
+ "2. A large blue rubber cube rotated 167.0° at 3D (1.80, 0.92, 0.70), pixel (292, 141, 10.0).\n",
+ "3. A small green rubber cylinder rotated 192.0° at 3D (-1.00, 0.00, 0.35), pixel (351, 177, 10.0).\n",
+ "4. A small red rubber cylinder rotated 271.0° at 3D (-2.00, -0.70, 0\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same material as the \"tiny gray thing.\" First, I need to identify which object is described as \"tiny gray thing\" in the scene.\n",
+ "\n",
+ "Looking through the list, there's a \"small gray metal cube\" mentioned. So that's one object. Now, I need to check all other objects to see if they're made of the same material (metal) as this cube.\n",
+ "\n",
+ "Let me go through each item:\n",
+ "\n",
+ "1. **Small red rubber sphere**: Material is rubber, so not the same.\n",
+ "2. **Large green metal cylinder**: Metal, but different shape and color.\n",
+ "3. **Large gray rubber sphere**: Rubber again, so no.\n",
+ "4. **Large red metal sphere**: Metal, but different size and color.\n",
+ "5. **Small red rubber sphere**: Same material as the first one.\n",
+ "6. **Large gray metal cube**: This is the one we're comparing against. So it's the same material.\n",
+ "7. **Small green rubber cylinder**: Rubber again.\n",
+ "8. **Large gray rubber sphere**: Rubber once more.\n",
+ "\n",
+ "So the only other object with the same material (metal) is the large gray metal cube. Therefore, there should be 1 such object\n",
+ "predicted:None\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the scene described. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small brown metal sphere...\" followed by its attributes like color, size, shape, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Starting with the first object: A small brown metal sphere. That's one item. Next is a large yellow rubber cube. That's two items. Then a small purple rubber cylinder. Third is another small brown metal sphere. Fourth is a small gray rubber cube. Fifth is a large green rubber cylinder. Sixth is a small yellow rubber sphere. Seventh is a large yellow metal cube. Eighth is a small yellow metal cube. So that's seven items total. There are no duplicates mentioned here. Each entry is distinct objects with different properties. Therefore, the answer should be 8.\n",
+ " 8 \n",
+ "predicted:8\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user provided a list of objects with their properties. Each entry starts with \"A small brown metal sphere...\" followed by its attributes. Let's count them one by one.\n",
+ "\n",
+ "First object: A small brown metal sphere. That's one item. Next is another small brown metal sphere. That's two items. Then a large gray metal sphere. Third is a large cyan rubber cylinder. Fourth is a large yellow metal cube. Fifth is a small green rubber cylinder. So that's five items total. The sixth is a large cyan rubber sphere. So adding all these up, the total number should be six. I'll check again to make sure there are no duplicates or any missing entries.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small cyan metal sphere...\" or \"A large blue rubber sphere...\" etc. So each of these is a separate object. There are six such entries. \n",
+ "\n",
+ "Each entry describes a distinct object with different attributes like color, size, shape, rotation, coordinates, and pixel coordinates. Since each entry is a separate item, that makes six items total. No duplicates mentioned. Therefore, the answer should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many rubber things are either tiny cyan blocks or tiny things in total. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to identify which objects are rubber and then check if they are either cyan and blocks or just small (small size) regardless of color or material.\n",
+ "\n",
+ "Looking at the scene:\n",
+ "\n",
+ "1. **Small cyan cylinder**: This is a rubber object. It's a cylinder, so not a block. So this doesn't count towards the answer because it's not a cyan block.\n",
+ "2. **Large brown metal sphere**: Not a rubber object, so no.\n",
+ "3. **Small gray metal cube**: Metal cube, not rubber. So not counted here.\n",
+ "4. **Small purple rubber cylinder**: Rubber, but it's purple, not cyan. So not part of the first category.\n",
+ "5. **Small cyan rubber cylinder**: This is a rubber object, but it's a cylinder, not a block. So not counted.\n",
+ "\n",
+ "So, the only rubber object that fits either category is the first one. Since there's only one such object, the answer should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user provided a list of objects with their properties. Each entry starts with \"A small gray metal sphere...\" followed by its attributes. Let's count them one by one.\n",
+ "\n",
+ "First object: A small gray metal sphere. That's one item. Next is another small gray metal sphere. That's two items. Then a large red metal sphere. Third is a small cyan rubber cylinder. Fourth is a small blue metal sphere. So that's three items total. The fifth is a large blue metal cube. That's four. So the answer should be 4.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of objects that are either cyan metallic things or small matte blocks. Let me go through each item one by one.\n",
+ "\n",
+ "First, I need to check each object in the scene. There are six items listed here. Let's start with the first one: a small purple rubber cylinder. It's not cyan or metallic, so doesn't count. Next, a large green rubber sphere. Not cyan or metallic, so no. Third is a small gray metal cube. That's a metal cube, but not cyan. Fourth is a large blue rubber cube. Cyan color but not a block, so no. Fifth is a small red rubber cylinder. Neither cyan nor a block. Sixth is a small yellow rubber cube. This one is a small matte (rubber) block, which fits the second part of the question. So that's one. Now, the last one is a small blue metal sphere. Metallic but not cyan. So that's zero. Adding them up, there's only one object that meets either category. Therefore, the answer should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each entry one by one.\n",
+ "\n",
+ "First line: \"A small brown rubber cube...\" That's one item. Next, \"A large gray metal cube...\" That's two items. Then, \"A small green rubber cylinder...\" Third item. Then, \"A small purple metal sphere...\" Fourth. Then, \"A small green metal cylinder...\" Fifth. And lastly, \"A large red rubber cylinder...\" Sixth. So that's six items in total. Each entry describes a separate object with different attributes like color, material, shape, size, rotation, coordinates, and pixel coordinates. Each of these entries is a distinct item. Therefore, the answer should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects have the same size as the \"shiny red sphere\". First, I need to identify all the objects in the scene and their sizes.\n",
+ "\n",
+ "Looking at the image, there are four objects listed. Let me list them out:\n",
+ "\n",
+ "1. A small cyan metal cylinder: 0.23 meters tall, located at certain coordinates.\n",
+ "2. A large purple metal cylinder: 0.78 meters tall.\n",
+ "3. A small green rubber cylinder: 0.19 meters tall.\n",
+ "4. A small red metal sphere: 0.15 meters tall.\n",
+ "\n",
+ "The object in question is the \"shiny red sphere\". The key here is to find other objects that are also small and made of metal. \n",
+ "\n",
+ "Looking through each object:\n",
+ "- The first one is a cylinder, so not a sphere. So no.\n",
+ "- Second is a cylinder, so not a sphere.\n",
+ "- Third is a cylinder again.\n",
+ "- Fourth is a sphere. It's small, but it's red, which matches the color. Its size is 0.15 meters, which is smaller than the other two cylinders (0.23 and 0.78). Therefore, this is the only\n",
+ "predicted:None\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of matte objects that are either small purple cubes or tiny cyan balls. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to identify all the objects in the scene and check their properties. The scene lists several items with different attributes like color, shape, size, rotation, coordinates, and pixel coordinates. \n",
+ "\n",
+ "Looking at each item:\n",
+ "\n",
+ "1. A large blue metal cube: Not relevant since it's a cube, not a sphere or cube.\n",
+ "2. Small brown metal cube: Also a cube, so not relevant.\n",
+ "3. Large blue metal sphere: Sphere, but not purple or cyan.\n",
+ "4. Small red rubber cylinder: Cylinder, not a cube or cyan ball.\n",
+ "5. Small cyan rubber sphere: This one is a sphere (ball) and cyan, which fits the second part of the question. So this counts as a \"tiny cyan ball\".\n",
+ "6. Small yellow metal sphere: Another sphere, but not cyan.\n",
+ "7. Small red rubber sphere: Sphere again, but red, so doesn given criteria.\n",
+ "8. Large brown metal cube: Cube, not a sphere or cyan ball.\n",
+ "9. Large red rubber cylinder: Cylinder, not a cube or cyan ball.\n",
+ "\n",
+ "So\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of large red objects in front of the large red object is less than the number of big green matte objects.\n",
+ "\n",
+ "First, I need to parse through each object listed and determine their attributes. Let me go through them one by one.\n",
+ "\n",
+ "The first object is a small brown rubber cube. Its color is brown, material is rubber, size is small. So that doesn't fit either category.\n",
+ "\n",
+ "Second: A small gray rubber sphere. Color is gray, so not relevant here.\n",
+ "\n",
+ "Third: A small blue rubber cube. Blue, rubber, small. Again, no.\n",
+ "\n",
+ "Fourth: A large red metal sphere. This is the large red object mentioned in the question. It's a sphere, so its shape is a sphere, which isn't a \"object\" as per the question. But since it's red and large, it counts as a large red object. So there's 1 such object.\n",
+ "\n",
+ "Fifth: A large green rubber cylinder. Green, rubber, cylinder. That's a big green matte object. So this is another count.\n",
+ "\n",
+ "Now, the question is comparing the number of large red objects (which is 1) with the number of big green matte objects (also 1). Since\n",
+ "predicted:None\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let's tackle this question step by step. The user is asking if the number of metallic cylinders in front of the purple rubber cylinder is greater than the number of tiny rubber objects to the left of the large green thing.\n",
+ "\n",
+ "First, I need to parse through each object in the scene provided. Let me list them out:\n",
+ "\n",
+ "1. A small yellow metal cube rotated 230.4° at 3D coordinates (0.78, -2.59, 0.36) with pixel coordinates (193, 111, 12.00).\n",
+ "2. A large cyan rubber cylinder rotated 232.3° at 3D (2.90, 0.00, 0.70) and pixel (306, 139, 10.00).\n",
+ "3. A small cyan rubber cylinder rotated 297.0° at 3D (-1.70, -1.59, 0.36) and pixel (202, 139, 10.00).\n",
+ "4. A large green metal cylinder rotated 233.2° at \n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many spheres are either big things or tiny cyan things. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to identify all the objects that are either \"big\" or \"tiny\" in size. Then, check if they are also cyan. \n",
+ "\n",
+ "Looking at the scene:\n",
+ "\n",
+ "1. **Large yellow metal cube**: This is a large object. So it's a big thing. But it's not cyan. So this doesn't count.\n",
+ "2. **Small brown metal sphere**: Small, but it's brown, so not cyan. So no.\n",
+ "3. **Large yellow metal cube**: Large, but it's yellow. Not cyan.\n",
+ "4. **Large green rubber cylinder**: Large, but green. Not cyan.\n",
+ "5. **Small cyan metal cylinder**: Small, but cyan. So this counts as a cyan thing.\n",
+ "6. **Large purple metal cylinder**: Large, but purple. Not cyan.\n",
+ "7. **Large yellow metal cube**: Large, but yellow. Not cyan.\n",
+ "\n",
+ "So the only object that fits both categories (big and cyan) is the small cyan metal cylinder. Therefore, there's 1 such object.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many objects are either small cyan metallic spheres or purple metal cylinders. Let me go through each category one by one.\n",
+ "\n",
+ "First, I need to check all the objects in the scene. There are eight items listed here. Each has attributes like color, material, shape, rotation, and coordinates. \n",
+ "\n",
+ "Starting with the first part: small cyan metallic spheres. Looking at each object:\n",
+ "\n",
+ "1. A small yellow cube (not cyan)\n",
+ "2. A large green metal sphere (green, not cyan)\n",
+ "3. A small purple rubber cylinder (purple, but not cyan)\n",
+ "4. A small cyan rubber sphere (cyan, but not metallic)\n",
+ "5. A small purple metal sphere (purple, metallic, but not small)\n",
+ "6. A small blue rubber cube (blue)\n",
+ "7. A small purple rubber cylinder (purple, rubber, not metal)\n",
+ "8. A small gray metal cube (gray)\n",
+ "\n",
+ "So, there's only one object that fits the first part: the small cyan rubber sphere. That's 1.\n",
+ "\n",
+ "Now, the second part: purple metal cylinders. Checking again:\n",
+ "\n",
+ "The only purple metal cylinder mentioned is the large purple metal cylinder. So that's 1 more.\n",
+ "\n",
+ "Adding both parts together: 1 +\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many shiny things are either green spheres or tiny red cylinders. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to identify which objects are green and spheres. Looking at the list:\n",
+ "\n",
+ "1. The first object is a small brown metal sphere. That's a sphere but not green, so no.\n",
+ "2. Second is a large gray rubber sphere. Not green.\n",
+ "3. Third is a small purple rubber cylinder. Cylinder, not a sphere.\n",
+ "4. Fourth is a large green metal cylinder. This is a cylinder, so not relevant.\n",
+ "5. Fifth is a small blue metal sphere. Sphere, but not green.\n",
+ "6. Sixth is a large yellow metal sphere. Sphere, but yellow, not green.\n",
+ "7. Seventh is a small red rubber cylinder. Cylinder, not a sphere.\n",
+ "\n",
+ "Now, checking for green spheres: There's only one green sphere (the third object), but it's a cylinder, so that doesn't count. So there's zero green spheres.\n",
+ "\n",
+ "Then, looking for tiny red cylinders: The seventh object is a small red rubber cylinder. Since it's a cylinder and red, this counts as a red cylinder. But since we're looking for both conditions (green sphere\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are in the described scene. Let me look at each line carefully.\n",
+ "\n",
+ "First line: \"A large purple rubber sphere...\" That's one item. Second line: \"A small yellow rubber sphere...\" That's two. Third: \"A small red rubber sphere...\" Three. Fourth: \"A small blue metal cube...\" Four. Fifth: \"A small brown rubber sphere...\" Five. Sixth: \"A large brown rubber cube...\" Six. Seventh: \"A large red metal cylinder...\" Seven. Eighth: \"A large blue rubber sphere...\" Eight. Ninth: \"A small gray metal cube...\" Nine. Tenth: \"A large brown rubber cube...\" Ten. \n",
+ "\n",
+ "So, counting all these, there are ten distinct items listed here. Each has different attributes like color, material, size, shape, rotation, coordinates, etc., but they're all separate objects. So total should be 10.\n",
+ " 10 \n",
+ "predicted:10\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many objects are in front of the tiny green cylinder and behind the tiny gray metallic block. First, I need to figure out which objects are in front of the green cylinder and which are behind the gray block.\n",
+ "\n",
+ "Looking at the scene:\n",
+ "\n",
+ "1. There's a small cyan metal sphere rotated 236.5° located at (-0.79, -2.48, 0.35). Its coordinates are (x, y, z).\n",
+ "2. A small gray metal cube at (0.37, -1.80, 0.35) with rotation 11.7°.\n",
+ "3. A small green rubber cylinder at (1.50, 0.70, 0.35).\n",
+ "\n",
+ "First, identify the objects that are in front of the green cylinder. The green cylinder has coordinates (1.50, 0.70, 0.35). So any object whose x or y coordinate is greater than 1.50 or less than 0.70 would be in front of it. But since there's no mention of depth in the image, we need to consider the camera's\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same color as the \"big metallic object\" in the scene.\n",
+ "\n",
+ "First, I need to identify which object is described as \"big metallic\". Looking through the list:\n",
+ "\n",
+ "The first line mentions a \"large yellow rubber cube\". The second says \"small brown metal cube\". The third is a \"large red metal sphere\". So the big metallic object here is the red one.\n",
+ "\n",
+ "Now, I need to check all other objects to see if any are also red. Let me go through each one:\n",
+ "\n",
+ "1. The small yellow rubber cube: No, that's yellow.\n",
+ "2. The large yellow rubber cube: Same color as the big metallic object (red), so not counted.\n",
+ "3. The small blue rubber cube: Blue, different color.\n",
+ "4. The small brown metal cube: Brown, different.\n",
+ "5. The large gray rubber cube: Gray, different.\n",
+ "6. The large red metal sphere: This is the same color as the big metallic object.\n",
+ "\n",
+ "So, only the red sphere matches. Therefore, there should be 1 other object with the same color.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are the same number of large gray shiny blocks on the left side of the metal ball as there are tiny yellow objects to the left of the blue metallic thing.\n",
+ "\n",
+ "First, I need to parse through each object in the scene. Let me list them all:\n",
+ "\n",
+ "1. A large cyan rubber cylinder...\n",
+ "2. A small purple rubber cylinder...\n",
+ "3. A large brown rubber sphere...\n",
+ "4. A small yellow rubber cube...\n",
+ "5. A large gray metal cube...\n",
+ "6. A large blue metal cube...\n",
+ "7. A large purple metal sphere...\n",
+ "\n",
+ "Now, the first part of the question is: \"large gray shiny blocks on the left side of the metal ball\". So, we need to find all large gray metal cubes (since they're shiny) and check their positions relative to the metal ball.\n",
+ "\n",
+ "Looking at the objects, the only large gray metal cube is the fifth one. Its coordinates are (-0.89, 1.90, 0.70). Now, the metal ball here would be the seventh object, which is a large purple metal sphere. So, the metal ball has coordinates (1.90, -2.80, 0.70).\n",
+ "\n",
+ "To\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are more tiny blue rubber objects compared to tiny purple rubber cylinders in the scene. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to identify all the objects mentioned in the scene. There are three objects described here: a large brown rubber sphere, a small cyan metal cylinder, and a small purple rubber cylinder.\n",
+ "\n",
+ "Now, the key terms here are \"tiny\" and \"rubber.\" So, we need to determine how many of these objects are both small (small) and made of rubber. \n",
+ "\n",
+ "Looking at the first object: a large brown rubber sphere. It's large, so not a tiny size. So this doesn given object does not count.\n",
+ "\n",
+ "Second object: small cyan metal cylinder. This is small but made of metal, not rubber. So it's not relevant.\n",
+ "\n",
+ "Third object: small purple rubber cylinder. This is small and made of rubber. That's a match for both criteria. \n",
+ "\n",
+ "So, there's 1 tiny (small) rubber object (the third one). Now, the question asks if this is greater than the number of tiny purple rubber cylinders. Since there's only one such object, it's not greater than zero. Therefore, the answer should be no\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user provided a list of objects with their properties. Each entry starts with \"A large cyan metal cylinder...\" followed by other attributes like color, size, material, rotation, coordinates, and pixel coordinates. There's also mentions of two spheres, two cubes, and a small red metal cylinder. \n",
+ "\n",
+ "First, I need to count each distinct objects listed here. Let me go through each line one by one.\n",
+ "\n",
+ "1. First object: A large cyan rubber sphere. That's one item.\n",
+ "2. Second is another large cyan metal cylinder. That's the second item.\n",
+ "3. Third is a small brown rubber cube. Third item.\n",
+ "4. Fourth is a large cyan metal cylinder. Fourth.\n",
+ "5. Fifth is a small yellow metal cube. Fifth.\n",
+ "6. Sixth is a large gray rubber sphere. Sixth.\n",
+ "7. Seventh is a large cyan metal sphere. Seventh.\n",
+ "\n",
+ "So, counting them all together: 1, 2, 3, 4, 5, 6, 7. That makes seven items total. So the answer should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking if the number of big metallic cylinders to the left of the big blue sphere is less than the number of gray rubber spheres.\n",
+ "\n",
+ "First, I need to identify all objects in the scene that match each category: \"big metallic cylinder\" and \"gray rubber sphere\". Let me list them out one by one.\n",
+ "\n",
+ "Looking at the first object: A large cyan metal cylinder. Since the color is cyan (not gray), size is large, material is metal, shape is cylinder. So this doesn't fit either category. Next one: a small brown metal cube. Again, not a cylinder or a sphere. Third object is a large green rubber sphere. That's a sphere, but it's green, so not gray. Fourth is a small yellow metal cube. Not a cylinder or sphere. Fifth is a large blue rubber sphere. This one matches both categories: large, metallic, and a sphere. \n",
+ "\n",
+ "So there's 1 big metallic cylinder (the first one) and 1 gray rubber sphere (the fifth one). Therefore, the comparison is 1 vs. 1. They're equal, so the answer should be no.\n",
+ " no \n",
+ "predicted:no\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many small red balls there are in the scene. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to look at each item listed. Each entry starts with \"A\" followed by attributes like color, size, shape, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Looking at the first object: A large cyan rubber sphere. That's a sphere, but its color is cyan, so that doesn't match the question. Next, a small yellow cube. Again, not a ball. Then a small red metal sphere. This one has a red color and is a sphere. So that's one. Finally, a small cyan metal cylinder. Not a ball. So the count should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many small cubes are made of the same material as the big gray rubber sphere. First, I need to identify all the objects in the scene that are small cubes and check their materials.\n",
+ "\n",
+ "Looking at the image, there are two small cubes mentioned: a small blue rubber cube and a small gray rubber cube. The big gray rubber sphere is described as large and made of rubber. So the question is whether either of these small cubes are also made of rubber.\n",
+ "\n",
+ "The first small cube is blue and made of rubber. The second one is gray and also made of rubber. Since both are small and both are made of rubber, they share the same material. Therefore, the answer should be 2.\n",
+ " 2 \n",
+ "predicted:2\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of green spheres in front of the small yellow block is less than the number of green spheres behind the tiny cyan matte object.\n",
+ "\n",
+ "First, I need to parse through each object in the scene. Let me list them out:\n",
+ "\n",
+ "1. A small purple metal sphere rotated 230.5° at 3D coordinates (0.79, -1.64, 0.38) and pixel coordinates (193, 100, 12.0).\n",
+ "2. A small blue rubber cube rotated 132.2° at 3D (0.32, 0.77, 0.38) and pixel (132, 132, 12.0).\n",
+ "3. A small yellow rubber sphere rotated 145.3° at 3D (-1.37, 1.73, 0.38) and pixel (292, 132, 12.0).\n",
+ "4. A small cyan rubber cylinder rotated 232.2° at 3D (1.70, -1.0\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many objects meet certain conditions. First, I need to parse the scene described in the image.\n",
+ "\n",
+ "There are four objects listed here:\n",
+ "\n",
+ "1. A large purple rubber cylinder rotated 238.4° located at specific coordinates.\n",
+ "2. A small brown rubber cube at different coordinates.\n",
+ "3. A small blue rubber cube at another set of coordinates.\n",
+ "4. A small purple rubber sphere at yet another position.\n",
+ "\n",
+ "The user wants to know how many cubes are either to the left of the blue cube (the third object) or things that are to the right of the small purple sphere (the fourth object).\n",
+ "\n",
+ "Let's break down each part step by step.\n",
+ "\n",
+ "First, identify the blue cube. Its coordinates are given as (-0.76, -2.59, 0.35). So, any cube that is to the left of this point would have an x-coordinate less than -0.76. Similarly, any object to the right of the purple sphere (x > 0.76) would also count.\n",
+ "\n",
+ "Now, check each cube against these criteria. The first cube is small brown, so its x-coordinate is 0.75. Since 0.7\n",
+ "predicted:None\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small green metal cylinder...\" or \"A large purple rubber cylinder...\" etc. So each of these entries seems to be separate objects. There are six such entries mentioned. \n",
+ "\n",
+ "Each entry describes a different object with distinct attributes like color, size, shape, rotation, coordinates, and pixel coordinates. Since each entry is a separate object, that makes six items total. There's no mention of any duplicates or groups that would reduce the count. Therefore, the answer should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other gray matte things share the same shape as the large gray cube. First, I need to identify all the objects in the scene that are gray and made of rubber (matte), and then check if their shape matches the large gray cube.\n",
+ "\n",
+ "Looking at the image, there are four cubes mentioned. Let me list them out:\n",
+ "\n",
+ "1. A small brown rubber cube rotated 230.6° located at certain coordinates.\n",
+ "2. A large blue metal cube rotated 145.8° with different coordinates.\n",
+ "3. A large brown rubber cube rotated 179.2° with different coordinates.\n",
+ "4. A large gray rubber cube rotated 232.3° with different coordinates.\n",
+ "\n",
+ "The first object is brown, so not gray. The second is blue, also not gray. The third is brown again. So only the fourth one is gray and made of rubber. But its shape is a cube, which is the same as the large gray cube. Therefore, there should be zero other gray matte cubes since the question asks for \"other\" ones. Wait, but wait, maybe there's another gray cube? Let me check again. The answer should be zero because the\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects have the same size as the purple one. First, I need to identify which object is purple. Looking through the list, the purple object is the \"large purple rubber cylinder\" at coordinates (-2.30, 1.96, 0.75). So its size is large.\n",
+ "\n",
+ "Now, I need to check all other objects to see if they're also large. Let me go through each one:\n",
+ "\n",
+ "1. The small yellow metal cube: size small. Not the same.\n",
+ "2. The large blue metal cube: same size as the purple one.\n",
+ "3. The large green metal sphere: different size.\n",
+ "4. The small red metal cylinder: different size.\n",
+ "5. The small gray metal cube: different size.\n",
+ "6. The large yellow rubber cube: different size.\n",
+ "\n",
+ "So, only the second object (the large blue metal cube) has the same size as the purple one. Therefore, there's 1 other object with the same size.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many big cyan objects are there in the described scene. Let me look through each item one by one.\n",
+ "\n",
+ "First, I need to check each object listed to determine if they are \"big\" and have a color of cyan. \n",
+ "\n",
+ "Looking at the image, there are several objects mentioned. Let's go through each one:\n",
+ "\n",
+ "1. A small red rubber cylinder: Not big and cyan.\n",
+ "2. Small purple rubber cylinder: Not big or cyan.\n",
+ "3. Large brown metal cube: Not cyan.\n",
+ "4. Large brown rubber sphere: Not cyan.\n",
+ "5. Small yellow rubber cube: Not cyan.\n",
+ "6. Large cyan rubber sphere: This one is big and cyan. So this counts as 1.\n",
+ "7. Large green metal cylinder: Not cyan.\n",
+ "8. Small red rubber sphere: Not cyan.\n",
+ "\n",
+ "So, only the sixth object is both large and cyan. Therefore, the answer should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of purple objects that are either large matte spheres or small cylinders. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to look at each item in the scene provided. There are three objects: a large gray rubber cube, a large purple rubber sphere, and a small yellow rubber cube. \n",
+ "\n",
+ "Looking at the first object: It's a large gray rubber cube. Since it's not purple and not a cylinder, it doesn't qualify. Next, the second object is a large purple rubber sphere. This one is both purple and a sphere, so it fits the criteria. So that's 1.\n",
+ "\n",
+ "Now, the third object is a small yellow rubber cube. Again, it's not purple and not a cylinder. Therefore, it doesn't count. \n",
+ "\n",
+ "So adding up the two qualifying objects (the purple sphere), the answer should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many matte objects share the same color as the metallic block. First, I need to identify all the matte objects in the scene and check their colors against the color of the metallic block.\n",
+ "\n",
+ "Looking at the image, there are several objects listed. Let me go through each one:\n",
+ "\n",
+ "1. A small yellow rubber cube: This is a matte object because it's made of rubber. Its color is yellow. The metallic block is also yellow. So this one matches.\n",
+ "\n",
+ "2. A large cyan rubber cylinder: Matte material, cyan color. Doesn't match.\n",
+ "\n",
+ "3. A small purple rubber cylinder: Matte, but purple. Not the same color as the metallic block.\n",
+ "\n",
+ "4. A small green metal cylinder: Metal, not matte. So no.\n",
+ "\n",
+ "5. A small gray rubber cube: Matte, gray. Doesn't match.\n",
+ "\n",
+ "6. A small purple metal cylinder: Metal, so not matte.\n",
+ "\n",
+ "7. A large green metal cylinder: Metal again, not matte.\n",
+ "\n",
+ "So, only the first object (yellow) is both matte and has the same color as the metallic block. Therefore, there's 1 such object.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small gray metal cube...\" or similar. There are six such entries: a small gray metal cube, a small cyan metal cylinder, a small red metal cylinder, a small purple rubber cylinder, a small yellow metal cube, a small red rubber cylinder, and a small gray rubber cube. \n",
+ "\n",
+ "Each of these entries describes a separate object. Even though some have similar attributes like color and material, they're all distinct objects. So each is a unique item. Therefore, counting them should be straightforward. Let me count them again to make sure:\n",
+ "\n",
+ "1. First cube\n",
+ "2. Second cube\n",
+ "3. Cylinder (cyan)\n",
+ "4. Cylinder (red)\n",
+ "5. Cylinder (purple)\n",
+ "6. Cube (yellow)\n",
+ "7. Cylinder (red)\n",
+ "8. Cube (gray)\n",
+ "\n",
+ "That's eight items total. No duplicates mentioned. So the answer should be 8.\n",
+ " 8 \n",
+ "predicted:8\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let's see. The user provided three objects in the scene described. Let me check each one to make sure they're distinct.\n",
+ "\n",
+ "First object: A small brown metal cube rotated 163.0° located at certain coordinates. That's one item. Second is a small green metal sphere with different rotation and coordinates. Third is a large red metal cylinder. Each of these have their own unique properties like color, size, shape, material, rotation, and coordinates. Since each has distinct attributes, they should be separate items. Therefore, the total number should be three.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's tackle this question step by step. The user is asking if the number of red cylinders on the left side of the tiny red thing is greater than the number of gray cubes to the left of the big red cylinder.\n",
+ "\n",
+ "First, I need to parse through each object in the scene and determine their attributes. Let me list them out:\n",
+ "\n",
+ "1. **Large purple metal sphere**: Size: large, color: purple, material: metal, rotation: 237.5°, coordinates: (-0.89, -2.46, 0.70)\n",
+ "2. **Small cyan metal cylinder**: size: small, color: cyan, material: metal, rotation: 232.2°, coordinates: (1.97, -1.90, 0.35)\n",
+ "3. **Small green metal sphere**: size: small, color: green, material: metal, rotation: 29.0°, coordinates: (0.70, -1.70, 0.35)\n",
+ "4. **Large gray rubber cube**: size: large, color: gray, material: rubber, rotation: 233.0°, coordinates: (1\n",
+ "predicted:None\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking for the number of gray balls that are in front of the small red rubber sphere to the left of the large yellow cube.\n",
+ "\n",
+ "First, I need to identify all objects in the scene and their attributes. Let's list them out:\n",
+ "\n",
+ "1. A large yellow metal cube rotated 230.4° at (-2.57, -1.96, 0.80) with pixel coordinates (193, 120, 0.35)\n",
+ "2. A small brown rubber cube rotated 132.2° at (3.20, -1.22, 0.35)\n",
+ "3. A small red rubber sphere rotated 233.5° at (2.30, 1.07, 0.35)\n",
+ "4. A large gray metal sphere rotated 233.5° at (2.50, -1.80, 0.75)\n",
+ "5. A small purple metal cylinder rotated 29.3° at (2.93, -1.27, 0.35)\n",
+ "\n",
+ "Now,\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user provided a list of objects with their properties. Each entry starts with \"A small yellow metal sphere...\" followed by its attributes. There's also a large blue rubber cube, a small brown rubber cube, another small yellow rubber cube, and a large yellow metal cube. \n",
+ "\n",
+ "Each entry is a separate object. So each one is a distinct item. Let me count them one by one.\n",
+ "\n",
+ "First: A small yellow metal sphere – that's one. Second, a small brown rubber cube – two. Third, another small yellow rubber cube – three. Fourth, a large yellow metal cube – four. That's five total. Therefore, there should be 5 items in the scene.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 6 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of small purple rubber cylinders that share the same shape as the purple shiny cylinder. First, I need to identify all the objects in the scene that are small, purple, and made of rubber. \n",
+ "\n",
+ "Looking through each item:\n",
+ "\n",
+ "1. The first object is a large green metal cylinder. That's not purple or small, so no.\n",
+ "2. Second one is a small green metal cylinder. Again, not purple or rubber.\n",
+ "3. Third is a small cyan metal cylinder. Not purple or rubber.\n",
+ "4. Fourth is a small purple rubber cylinder. This one matches both color and material (purple) and shape (cylinder). So this counts as 1.\n",
+ "5. Fifth is a large purple metal cylinder. This is the one we're comparing against.\n",
+ "\n",
+ "So there's only one small purple rubber cylinder that shares the same shape with the purple shiny cylinder. Therefore, the answer should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many big rubber objects share the same shape as the small blue metal cube. First, I need to identify all the objects in the scene that are big and made of rubber. Let me list them out.\n",
+ "\n",
+ "Looking at each object:\n",
+ "\n",
+ "1. A large brown rubber sphere: This one is a sphere. The question mentions \"big\" and \"rubber,\" so this fits both criteria. So count 1.\n",
+ "2. A large yellow rubber cube: Another big rubber object here. So that's another one.\n",
+ "3. A large purple rubber sphere: Third item, also big and rubber. That's the third one.\n",
+ "4. A small blue metal cube: This is the fourth object. It's small and metal, not rubber. So doesn't count.\n",
+ "5. A large yellow rubber cube: Fifth object, but it's a cube, which is different from the small cube mentioned in the question. So no.\n",
+ "\n",
+ "So, there are three big rubber objects (spheres) that match the description. Therefore, the answer should be 3.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many gray metallic cylinders there are in the scene. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to look through all the objects listed here. Each entry starts with \"A small\" or \"A large\", followed by attributes like color, material, shape, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Looking at each item:\n",
+ "\n",
+ "1. A small green metal cylinder: Green, so not gray. So this doesn't count.\n",
+ "2. A large yellow rubber cube: Yellow, so not gray.\n",
+ "3. A large blue metal sphere: Blue, no.\n",
+ "4. Small red rubber sphere: Red, not gray.\n",
+ "5. Small purple metal sphere: Purple, not gray.\n",
+ "6. Small gray metal cube: This one has a gray color and is made of metal. That's the only one that matches both criteria. So that's one.\n",
+ "7. Small yellow metal sphere: Yellow, so not gray.\n",
+ "8. Large yellow rubber cube: Yellow again, not gray.\n",
+ "\n",
+ "So after checking all entries, there's only one object that is both small and gray and made of metal. Therefore, the answer should be 1.\n",
+ " 1 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many small things are behind the blue sphere. First, I need to identify all the objects in the scene and their properties.\n",
+ "\n",
+ "Looking at the image, there are several items listed. Let me go through each one:\n",
+ "\n",
+ "1. A large cyan metal cylinder. Small size?\n",
+ "2. A small purple metal cylinder. Small?\n",
+ "3. A large brown metal cube. Large?\n",
+ "4. A small brown rubber cube. Small?\n",
+ "5. A small yellow metal cube. Small?\n",
+ "6. A large purple rubber cylinder. Large?\n",
+ "7. A small blue rubber sphere. Small?\n",
+ "\n",
+ "The blue sphere is item 7. Now, I need to determine which of the other objects are \"small\" and are located behind this sphere. \n",
+ "\n",
+ "First, check the positions. The scene lists coordinates for each object. For example, the first object has (0.98, -2.83, 0.35) and the seventh one is (2.93, 1.90, 0.70). The sphere is at (2.93, 1.90, 0.70), so its depth (z-coordinate) is 0.70\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small cyan rubber sphere...\" followed by its attributes. There's another entry for a large yellow rubber sphere, then a small green rubber cylinder, a large yellow metal cube, and a small blue metal sphere. \n",
+ "\n",
+ "Each of these entries describes a separate object. So each has distinct properties like color, size, material, shape, rotation, 3D coordinates, and pixel coordinates. Since each entry is a separate item, that makes five items total. Therefore, the answer should be 5.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 6 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of shiny objects that share the same color as the small cyan rubber cylinder. First, I need to identify all the objects in the scene and check their colors.\n",
+ "\n",
+ "Looking at the image, there are two objects: a large cyan metal cylinder and a small cyan rubber cylinder. The question specifies \"shiny\" which likely refers to the material. So, we need to determine if either of these cylinders are made of metal (which would be shiny) or rubber (which would be non-shiny).\n",
+ "\n",
+ "The first object is a \"large cyan metal cylinder\". Since it's described as metal, it's shiny. The second object is a \"small cyan rubber cylinder\". Rubber is not typically considered shiny unless it's made from a reflective material like plastic with a glossy finish. Therefore, this one is not shiny. \n",
+ "\n",
+ "So, only the first object is both cyan and made of metal, which makes it shiny. The other is not. Thus, the answer should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same color as the \"shiny blue cube\". First, I need to identify the object that is described as \"shiny blue\" in the scene.\n",
+ "\n",
+ "Looking through the image, there's only one object mentioned: a small cyan metal cylinder. The other objects are a small blue rubber cube and another small blue metal cube. The question is asking for other objects of the same color (shiny blue) apart from the first one. Since the first object is already specified as \"shiny\", we don't count it again. So, the other object that matches this description would be the second one. Therefore, there should be 1 such object.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small yellow rubber cube...\" or similar. There are six entries here. Each has different attributes like color, size, shape, rotation, coordinates, and pixel coordinates. \n",
+ "\n",
+ "Let's count them:\n",
+ "\n",
+ "1. A large gray metal sphere...\n",
+ "2. A small yellow rubber cube...\n",
+ "3. A large green metal sphere...\n",
+ "4. A small gray rubber cube...\n",
+ "5. A small cyan rubber cylinder...\n",
+ "6. A large red rubber cylinder...\n",
+ "7. A large yellow metal cube...\n",
+ "\n",
+ "Each of these is a separate object. Even though some have similar properties (like two cubes), they're distinct objects. So each entry counts as one item. Therefore, total should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many objects are to the right of the big gray cube and left of the large cyan matte cylinder. First, I need to figure out which objects are in each category.\n",
+ "\n",
+ "Looking at the scene:\n",
+ "\n",
+ "1. **Big gray cube**: The first object is a \"large gray metal cube\" located at (-2.73, 0.98, 0.75). So that's one object.\n",
+ "\n",
+ "2. **Large cyan matte cylinder**: The third object is a \"large cyan rubber cylinder\" at (2.46, -1.70, 0.75). That's another one.\n",
+ "\n",
+ "Now, the question is about objects that are to the right of the cube and left of the cylinder. Let's break this down step by step.\n",
+ "\n",
+ "First, identify the cube's coordinates: (-2.73, 0.98, 0.75). The \"right\" direction would be along the x-axis. So any object with an x-coordinate greater than -2.73 but less than the cube's x-coordinate would be to the right of the cube.\n",
+ "\n",
+ "Next, the cylinder's coordinates are (2.46, -1.7\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the scene described. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small brown metal cube...\" followed by its attributes like size, color, rotation, coordinates, and pixel coordinates. There's another entry for a large yellow metal cube. Then two more entries for small purple rubber cylinders. So that's five items total. The fifth entry is a large purple rubber cylinder. That's the sixth item. So adding them up: 1 + 1 + 2 + 2 + 6 = 10. Therefore, there should be ten items in total.\n",
+ " 10 \n",
+ "predicted:10\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each entry one by one.\n",
+ "\n",
+ "First line: \"A small yellow metal cube...\" That's one item. Next, \"A small gray rubber cube...\" That's two items. Then, \"A small brown metal cube...\" Third item. Finally, \"A small red rubber cylinder...\" Fourth item. So that's four items total. Each entry describes a separate object with different attributes like color, material, shape, rotation, coordinates, and pixel coordinates. Since each of these entries is a distinct object, they should count as individual items. Therefore, the answer should be 4.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of red objects behind the sphere is the same as the number of purple rubber balls. First, I need to parse the scene described.\n",
+ "\n",
+ "There are three objects listed here: two gray cubes and one red sphere. The user wants to know how many red objects are behind the sphere and compares that to the number of purple rubber balls.\n",
+ "\n",
+ "Let's start by identifying the red object. The only red object mentioned is the sphere. So there's 1 red object.\n",
+ "\n",
+ "Now, the question is about objects behind the sphere. But in the scene, all three objects are on the same plane (the ground plane). There's no mention of any depth or distance. Therefore, each object is at the same position relative to the camera. Since they're all on the same plane, none of them would be behind the sphere. So the count of red objects behind the sphere is zero.\n",
+ "\n",
+ "Next, the question asks for purple rubber balls. Wait, but there's no purple object mentioned in the scene. All objects are either gray cubes or the red sphere. So there are zero purple objects. Therefore, the count of purple rubber balls is also zero.\n",
+ "\n",
+ "Comparing these two counts: 0 (red objects behind\n",
+ "predicted:None\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at each entry one by one.\n",
+ "\n",
+ "First line: \"A large green metal sphere...\" That's one item. Next, \"A small purple metal cylinder...\" That's two items. Then, \"A small cyan rubber sphere...\" Third item. And lastly, \"A large purple rubber cylinder...\" Fourth item. So that makes four total. Each of these entries describes a distinct object with different attributes like color, size, material, shape, rotation, and coordinates. Therefore, there should be four items in the scene.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many other objects share the same color as the \"rubber ball\". First, I need to identify which object is the rubber ball. Looking through each item listed:\n",
+ "\n",
+ "1. The first object is a small brown metal sphere. That's the rubber ball mentioned in the question. So the color here is brown.\n",
+ "\n",
+ "Now, I need to check all other objects to see if any have the same color. Let me list them again for clarity:\n",
+ "\n",
+ "- A large purple rubber cylinder (purple)\n",
+ "- Small green rubber sphere (green)\n",
+ "- Large red rubber cylinder (red)\n",
+ "- Small yellow metal cube (yellow)\n",
+ "- Small brown metal sphere (brown)\n",
+ "\n",
+ "The question is about the rubber ball, which is the first object. So we're looking for other objects that are also rubber. The only other rubber object is the large red rubber cylinder. Therefore, there should be one other object with the same color.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small gray metal cube...\" or similar. There are six such entries: a large brown metal cube, a small cyan rubber sphere, a small gray metal cube, a small red rubber sphere, a small cyan rubber cylinder, a large green metal sphere, and a large yellow metal sphere. \n",
+ "\n",
+ "Each of these entries describes a separate object. Even though some have similar attributes like color and material, they are distinct objects with different coordinates and rotations. So each is a unique item. Therefore, counting them all together should give us six items.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of tiny spheres to the left of the small green rubber sphere is the same as the number of tiny red metal objects.\n",
+ "\n",
+ "First, I need to parse the scene described in the image provided. There are three objects listed here: a large yellow metal sphere, a small blue rubber cube, and a small green rubber sphere. \n",
+ "\n",
+ "The key points here are:\n",
+ "\n",
+ "1. The first object is a large yellow metal sphere. Its size is large, color yellow, material metal, shape sphere. So this doesn't count towards either category because it's not a tiny sphere or a red metal object.\n",
+ "2. The second object is a small blue rubber cube. This is a cube, so it's not a sphere. Therefore, it also doesn't qualify for either category.\n",
+ "3. The third object is a small green rubber sphere. This one is a sphere, but its color is green, which isn't red. So it's a tiny sphere, but not red or metal.\n",
+ "\n",
+ "Now, the question is comparing the number of tiny spheres (small spheres) to the number of tiny red metal objects. Let's break down each part separately.\n",
+ "\n",
+ "Tiny spheres: The only tiny sphere mentioned is the green one.\n",
+ "predicted:None\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same color as the \"tiny metal sphere.\" First, I need to identify which object is the \"tiny metal sphere\" in the scene.\n",
+ "\n",
+ "Looking through the list:\n",
+ "\n",
+ "1. A small cyan metal cylinder: Not a sphere.\n",
+ "2. A large brown metal sphere: This one is a sphere but not tiny. So that's not relevant.\n",
+ "3. A small red rubber sphere: That's another sphere, but different colors.\n",
+ "4. A small blue metal sphere: This is the one we're looking for. It's a sphere and made of metal, so its color is blue.\n",
+ "5. A large blue metal cube: Cube, not a sphere.\n",
+ "\n",
+ "So the only other object that's a sphere and has the same color (blue) is the small blue metal sphere. Therefore, there should be 0 other objects with the same color.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same material as the purple metal sphere. First, I need to identify which object is made of the same material as the purple metal sphere.\n",
+ "\n",
+ "Looking at the scene, there's one purple metal sphere mentioned: \"A large purple metal sphere rotated 120.3° located at specific coordinates. So its material should be metal. Now, I need to check all other objects to see if any others are also made of metal.\n",
+ "\n",
+ "Let's go through each object one by one:\n",
+ "\n",
+ "First object: A small brown rubber cube. Material here is rubber. That's not metal. So this doesn't match.\n",
+ "\n",
+ "Second object: Large green rubber sphere. Rubber again. No.\n",
+ "\n",
+ "Third object: Small gray rubber cube. Again, rubber.\n",
+ "\n",
+ "Fourth object: Large green metal cylinder. This one is made of metal. So that's another one. \n",
+ "\n",
+ "The fifth object is the purple metal sphere itself. Since we're looking for other objects with the same material, this one counts as the same material. Therefore, there are two objects made of metal.\n",
+ " 2 \n",
+ "predicted:2\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of small cyan rubber cylinders on the left side of the gray object is greater than the number of large brown matte cylinders. \n",
+ "\n",
+ "First, I need to parse through each object in the scene. Let me list them out:\n",
+ "\n",
+ "1. A large green rubber cylinder rotated 230.7° at 3D coordinates (2.56, -0.98, 0.74) and pixel coordinates (333, 191, 12.0).\n",
+ "2. A large blue metal cube rotated 133.3° at 3D (-1.97, 1.92, 0.74) and pixel (237, 129, 10.0).\n",
+ "3. A small cyan rubber cylinder rotated 233.7° at 3D (1.70, -1.98, 0.32) and pixel (287, 191, 10.0).\n",
+ "4. A large blue metal cube rotated 273.7° at 3D (-2.97, 1.9\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of big blue blocks to the right of the large rubber sphere is greater than the number of big purple rubber objects.\n",
+ "\n",
+ "First, I need to parse through each object in the scene. Let me list them out:\n",
+ "\n",
+ "1. A small brown metal cube rotated 235.6° at 3D coordinates (0.78, -2.49, 0.35) and pixel coordinates (193, 131, 11.0).\n",
+ "2. A large green metal sphere at (-2.70, 2.70, 0.70) and pixel (232, 107, 10.0).\n",
+ "3. A large blue rubber cube at (1.90, -0.70, 0.35) and pixel (139, 139, 11.0).\n",
+ "4. A small brown metal cube at (2.30, 1.80, 0.35) and pixel (292, 139, 11.0).\n",
+ "5. A large purple rubber cylinder at (\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of small objects that are either red or yellow metal cylinders. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to check each item in the scene. There are six objects listed here: a large cyan rubber cylinder, a small purple metal sphere, a large brown rubber cube, a small blue rubber cube, a large yellow metal sphere, a small purple metal cylinder, and a large red rubber cylinder.\n",
+ "\n",
+ "Looking at each object:\n",
+ "\n",
+ "1. The first object is a large cyan rubber cylinder. It's not small, so it doesn't qualify. So no points here.\n",
+ "2. Second is a small purple metal sphere. Small but not red or yellow. So no.\n",
+ "3. Third is a large brown rubber cube. Not small or yellow or metal. So no.\n",
+ "4. Fourth is a small blue rubber cube. Small, but not red or yellow or metal. So no.\n",
+ "5. Fifth is a large yellow metal sphere. Large, not small. So no.\n",
+ "6. Sixth is a small purple metal cylinder. Small, and it's purple, which isn't red or yellow. So no.\n",
+ "7. Seventh is a large red rubber cylinder. Small, red, and\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The user provided a list of objects described in the scene. Each entry starts with \"A small yellow metal cube...\" followed by other attributes like color, size, rotation, coordinates, and pixel coordinates. There are six such entries listed here. Each entry describes a different object. So each is a separate item. Therefore, counting them should be straightforward. Let me count them one by one.\n",
+ "\n",
+ "First: A large blue rubber cube. That's one. Second: A large green metal cylinder. Second. Third: Small green rubber sphere. Third. Fourth: Large yellow metal sphere. Fourth. Fifth: Small gray metal sphere. Fifth. Sixth: Small brown rubber sphere. Sixth. Seventh: Small yellow metal cube. Seventh. \n",
+ "\n",
+ "So that's seven items total. Each entry is a distinct object. No duplicates mentioned. Therefore, the answer should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are more purple metallic things than big red metal things. First, I need to count each category separately.\n",
+ "\n",
+ "Looking at the scene, there are several objects listed. Let me go through each one:\n",
+ "\n",
+ "1. A large yellow rubber sphere: not relevant since color and material don't match.\n",
+ "2. A small cyan rubber sphere: same issue here.\n",
+ "3. A large purple metal cylinder: This is a purple metallic object. So that's 1.\n",
+ "4. A large purple metal sphere: Another purple metallic thing. That's 2.\n",
+ "5. A small green metal cylinder: Green, so not counted.\n",
+ "6. A large purple rubber cylinder: Purple but rubber, so not counted.\n",
+ "7. A small cyan rubber cylinder: Cyan, so not counted.\n",
+ "\n",
+ "So, there are two purple metallic things (the first and second items). Now, for big red metal things. There's no mention of any red objects in the scene. All the colors mentioned are purple, yellow, cyan, and green. Therefore, there are zero big red metal things. Comparing these two numbers, 2 (purple) vs. 0 (red), so yes, the number of purple metallic things is greater than\n",
+ "predicted:None\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let's see. The user provided a scene with three objects. Let me check each object one by one.\n",
+ "\n",
+ "First, I'll start with the first object: \"A small cyan metal sphere\". That's a sphere made of metal. Its size is small, color cyan, and located at certain coordinates. Next, the second object is a large cyan metal cylinder. It's also a cylinder, but different from the first one. Finally, the third is a small purple rubber cylinder. So that's three distinct objects. Each has their own unique properties like color, material, shape, and coordinates. Therefore, the total number should be 3.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 4 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same color as the \"tiny brown metal sphere\". First, I need to identify which object is described as \"tiny brown metal sphere\" in the scene.\n",
+ "\n",
+ "Looking through each object listed:\n",
+ "\n",
+ "1. The first object is a small green rubber sphere. Its color is green, so that's not the one we're looking for.\n",
+ "2. The second is a large yellow metal cube. Yellow isn't brown, so this is different.\n",
+ "3. The third is a small brown metal sphere. This matches the description. So that's one match.\n",
+ "4. The fourth is a small brown rubber sphere. This is another brown sphere, but it's rubber, so that's a different material. So no.\n",
+ "5. The fifth is a large purple rubber cylinder. Purple doesn't match either.\n",
+ "\n",
+ "So, only the third object is brown and made of metal. Therefore, there should be 1 other object with the same color (brown) as the specified sphere.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user provided a list of objects with their properties. Each entry starts with \"A small purple rubber cylinder...\" followed by its attributes. Let's count them one by one.\n",
+ "\n",
+ "First item: A small purple rubber cylinder. That's one object. Next is another small purple rubber cylinder. That's two. Third is a large yellow rubber cube. Fourth is a small brown metal sphere. Fifth is a small red metal cylinder. Sixth is a small brown metal sphere. So that's five objects. The seventh is a small purple rubber cylinder again. So adding all these up, that makes six total. Therefore, there should be 6 items in the scene.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many objects are either yellow things or large red cylinders. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to check if any of the objects are yellow. Looking at the list:\n",
+ "\n",
+ "1. A small blue metal sphere: Not yellow.\n",
+ "2. Large purple metal cylinder: Not yellow.\n",
+ "3. Small cyan rubber cylinder: Not yellow.\n",
+ "4. Large red metal cylinder: This one is both large and red, so that counts as a candidate.\n",
+ "5. Small yellow metal cube: Yes, this is yellow.\n",
+ "6. Small blue rubber sphere: Not yellow.\n",
+ "7. Small cyan metal cylinder: Not yellow.\n",
+ "\n",
+ "So, there are two objects that meet the criteria: the large red cylinder (object 4) and the small yellow cube (object 5). Therefore, the answer should be 2.\n",
+ " 2 \n",
+ "predicted:2\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many cylinders meet certain criteria. So first, I need to look through all the objects in the scene.\n",
+ "\n",
+ "Looking at each object:\n",
+ "\n",
+ "1. A large gray metal cube. Not a cylinder.\n",
+ "2. A small gray metal sphere. Cylinder?\n",
+ "3. A small gray rubber cube. Cylinder?\n",
+ "4. A small purple metal sphere. Cylinder?\n",
+ "5. A large green rubber cylinder. Cylinder.\n",
+ "6. A small gray metal sphere. Cylinder?\n",
+ "\n",
+ "Now, the criteria are \"either small purple objects or tiny metal things\". Let's break that down:\n",
+ "\n",
+ "- Small purple objects: The only small purple object here is the small purple metal sphere (object 4). But wait, the question says \"tiny metal things\", which might be a typo for \"small metal things\". So maybe \"small\" refers to size, and \"metal\" refers to material. So any small metal object would count. That would include the small gray metal sphere (object 2) and the small purple metal sphere (object 4).\n",
+ "\n",
+ "So the two cylinders that fit this criterion are the large green rubber cylinder (object 5) and the small gray metal sphere (object 2). Therefore, there should be 2 cylinders meeting the\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are an equal number of shiny cylinders behind the small rubber ball and cylinders in total.\n",
+ "\n",
+ "First, I need to parse the scene described. There are four objects listed: a large blue rubber cube, a small brown rubber sphere, a small cyan metal cylinder, and a large cyan metal cylinder.\n",
+ "\n",
+ "The key here is identifying which cylinders are shiny. Since both the small cyan metal cylinder and the large cyan metal cylinder are made of metal, they would be considered \"shiny\" based on typical material properties. However, the question specifies \"shiny cylinders,\" so we should count only those that are metal. \n",
+ "\n",
+ "Looking at each object:\n",
+ "\n",
+ "1. Large cyan metal cylinder: Shiny.\n",
+ "2. Small cyan metal cylinder: Shiny.\n",
+ "3. Large blue rubber cube: Not a cylinder.\n",
+ "4. Small brown rubber sphere: Not a cylinder.\n",
+ "\n",
+ "So, there are two shiny cylinders (the two metal ones). Now, the question asks for the number of cylinders behind the small rubber ball. The small rubber ball is the small brown rubber sphere. So, we need to determine how many cylinders are behind this sphere.\n",
+ "\n",
+ "In the scene, all cylinders are either large or small. The large cyan metal cylinder is in\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of brown objects that are either small cubes or large blocks. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to check each item in the scene. Each entry has attributes like color, size, shape, rotation, coordinates, and pixel coordinates. The relevant attributes here are color and size (small or large). \n",
+ "\n",
+ "Looking at the first object: a large gray metal cube. Its color is gray, so not brown. So this doesn't count.\n",
+ "\n",
+ "Second object: small blue metal cube. Color is blue, so no.\n",
+ "\n",
+ "Third: small gray rubber sphere. Not brown.\n",
+ "\n",
+ "Fourth: large red rubber cylinder. Red, so not brown.\n",
+ "\n",
+ "Fifth: small brown metal sphere. This one is brown and small, but the question asks for \"large blocks\" which is a different category. So this doesn't count.\n",
+ "\n",
+ "Sixth: large blue metal cube. Blue, so not brown.\n",
+ "\n",
+ "Seventh: large brown metal sphere. This is brown and a sphere, but the question is about cubes. Since it's a sphere, it's not a cube. Therefore, it doesn't count.\n",
+ "\n",
+ "So adding up all the brown objects that are either small cubes or large blocks: only\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many small red metallic objects share the same shape as the large gray rubber sphere. First, I need to identify all the objects that are small and red in color. Then, among those, check if their shape matches the large gray rubber sphere.\n",
+ "\n",
+ "Looking through each object:\n",
+ "\n",
+ "1. The first object is a large purple rubber cylinder. Not relevant since it's purple and not red.\n",
+ "2. Second one is a small green rubber cylinder. No, different colors and shapes.\n",
+ "3. Third is a small red metal cylinder. This one is red and has a cylinder shape. The question asks for the same shape as the large gray rubber sphere. The sphere is made of rubber, so this doesn given object isn't a sphere. So no match here.\n",
+ "4. Fourth is a large purple rubber cylinder. Again, not red or a sphere.\n",
+ "5. Fifth is a small green rubber cylinder. Same issue here.\n",
+ "6. Sixth is a large brown rubber sphere. Different material and color.\n",
+ "7. Seventh is a small red metal cylinder. This one is red and has the same shape (cylinder) as the large gray rubber sphere. So this is the only one that fits both criteria.\n",
+ "8. Eighth is\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if the number of brown rubber spheres is less than the number of big blocks. First, I need to figure out how many brown rubber spheres there are and how many big blocks there are.\n",
+ "\n",
+ "Looking at the scene, there are three objects: a large cyan rubber cylinder, a large brown rubber cube, and a large brown rubber sphere. \n",
+ "\n",
+ "First, let's count the brown rubber spheres. There's one brown rubber sphere mentioned in the scene. So that's 1 brown rubber sphere.\n",
+ "\n",
+ "Next, the big blocks. The two big blocks are described as \"large gray rubber cube\" and \"large brown rubber cube\". That's two big blocks.\n",
+ "\n",
+ "Now, comparing these two numbers: 1 (brown rubber sphere) vs. 2 (big blocks). Since 1 is less than 2, the answer should be no.\n",
+ " no \n",
+ "predicted:no\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at each object listed one by one.\n",
+ "\n",
+ "First, there's a large red metal cylinder. That's one item. Next, a small brown metal cube. That's two items. Then, a small purple metal cylinder. Third, a large cyan metal sphere. So that's three items. The fourth is a small purple metal cylinder again. Wait, but wait, the question is asking for distinct items. Even though the third and fourth are both cylinders, they have different coordinates and rotations, so they're separate. Therefore, total of four items.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 4 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of big things that are behind the large blue rubber sphere. First, I need to identify all the objects in the scene and their properties.\n",
+ "\n",
+ "Looking at the image, there are seven objects listed. Let me go through each one:\n",
+ "\n",
+ "1. A small purple metal sphere: This is a small object, so not relevant.\n",
+ "2. A large gray rubber cube. So this is a big object, but it's a cube, not a sphere. So no.\n",
+ "3. Large brown rubber cube. Again, a cube, so not a sphere.\n",
+ "4. Small green metal cylinder. Cylinder, not a sphere.\n",
+ "5. Large cyan rubber sphere. This is a sphere, but it's cyan, so not the right color.\n",
+ "6. Large gray metal sphere. This is a sphere, but its color is gray, which doesn't match the question. So not relevant.\n",
+ "7. Large red rubber cylinder. Cylinder again, not a sphere.\n",
+ "8. Large blue rubber sphere. This is a sphere, but it's blue, which matches the color specified in the question. Since it's a large object, it should be counted.\n",
+ "\n",
+ "So, only the last item is a large blue rubber sphere. Therefore\n",
+ "predicted:None\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user provided a list of objects with their properties. Each entry starts with \"A small brown rubber sphere...\" followed by other attributes like color, size, rotation, coordinates, and pixel coordinates. There's also a large gray metal cube, a large yellow rubber sphere, a small red rubber cylinder, a large purple metal cylinder, a small yellow metal cube, and a large gray rubber cube. \n",
+ "\n",
+ "Each of these entries is a separate object. Let me count them one by one.\n",
+ "\n",
+ "First: A small brown rubber sphere. That's 1. Second, another small brown rubber sphere. That's two. Third, a large yellow rubber sphere. Third item. Fourth, a large purple metal cylinder. Fifth, a small yellow metal cube. Sixth, a large gray metal sphere. Seventh, a large gray rubber cube. So that's seven items total. Therefore, the answer should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let's see. The user provided three objects described in the scene. Each entry starts with \"A small cyan rubber cylinder...\" followed by its attributes. Let me count them one by one.\n",
+ "\n",
+ "First object: A small cyan rubber cylinder rotated 162.3° located at certain coordinates. That's one item. Second object is another small cyan rubber cylinder, different rotation and coordinates. Third is a large blue rubber cube. So that's three items total. No other objects mentioned. Therefore, the answer should be 3.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 4 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many brown rubber objects are in front of the cyan cylinder and to the right of the small yellow sphere. Let me break this down step by step.\n",
+ "\n",
+ "First, I need to identify all the brown rubber objects. Looking through the list, there are two: a large brown rubber cube and another small brown rubber cube. So that's two brown rubber objects.\n",
+ "\n",
+ "Next, I need to find which of these are in front of the cyan cylinder. The cyan cylinder is mentioned as a \"small cyan metal cylinder\" located at certain coordinates. So I need to check if any of the brown rubber objects are behind or to the left of this cylinder. Since the question says \"in front of,\" that would mean they should be to the right (assuming the coordinate system is right-handed). But wait, the question is about the position relative to the cyan cylinder. So if an object is in front of the cylinder, its coordinates must be further away from the cylinder than the cylinder itself. Wait, but the question is about the \"right side\" of the small yellow sphere. The small yellow sphere is at some coordinates. So the brown rubber objects need to be to the right of this sphere. Wait, but the sphere\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small brown metal cube...\" followed by its attributes like size, color, rotation, coordinates, and pixel coordinates. \n",
+ "\n",
+ "Starting with the first item: A small brown metal cube rotated 132.7° located at certain coordinates. That's one item. Next, another small brown metal cube, different coordinates. Third is a large yellow metal sphere. Fourth is a small brown rubber cube. Fifth is a large brown metal sphere. Sixth is a small gray rubber cube. Seventh is a large purple metal cylinder. So that's six items in total. There are no other entries mentioned. Therefore, the answer should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other small shiny objects share the same shape as the gray metallic cube. First, I need to identify all the objects in the scene that are small and made of metal.\n",
+ "\n",
+ "Looking at the image, there are three objects: a large blue rubber cube, a small yellow metal sphere, and a large gray metal cube. \n",
+ "\n",
+ "The question specifies \"other\" which implies excluding the one mentioned in the question. So we need to exclude the gray metallic cube itself. \n",
+ "\n",
+ "So, the other two objects are the small yellow metal sphere and the large blue rubber cube. Since both are cubes, they have the same shape. Therefore, there should be 1 such object.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many large rubber cylinders are there in the scene. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to look at each item listed. The first object is a \"large gray metal sphere\". That's a sphere, not a cylinder. So that doesn't count. Next, there's a small yellow rubber cube. Again, this is a cube, so no. Then, a large red rubber sphere. This is a sphere again. So that's two spheres. The last object is a large cyan rubber cylinder. Wait, the question asks for large rubber cylinders. The material here is rubber, but the shape is a cylinder. So this counts as one. Therefore, the total number of large rubber cylinders should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects have the same size as the brown cube. First, I need to identify the brown cube in the scene.\n",
+ "\n",
+ "Looking at the image, there's a \"large brown metal cube\" mentioned. So the cube here is large. Now, I need to check if any other objects share this same size.\n",
+ "\n",
+ "Let me list all the objects again: a small green rubber cylinder, a large red metal cylinder, and a small green metal cylinder. The first object is small, the second is large, and the third is also small. Since the question is about the brown cube (which is large), we need to count how many other objects are also large.\n",
+ "\n",
+ "The only other object that's large is the red metal cylinder. Therefore, there should be one other object of the same size.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many big rubber objects share the same color as the big matte cylinder. First, I need to identify the big rubber object in the scene.\n",
+ "\n",
+ "Looking at the image, there are three objects listed: a small brown rubber sphere, a large cyan rubber cylinder, and a small blue metal cube. The question mentions \"big rubber objects,\" so we're looking for any that are both large and made of rubber. \n",
+ "\n",
+ "The large rubber object here is the cyan cylinder. Now, checking all other objects:\n",
+ "\n",
+ "- The first object is a small brown rubber sphere. That's not a big one, so it doesn't count.\n",
+ "- The second object is a large cyan rubber cylinder. This matches the size (large) and material (rubber). So this is the one we're comparing against.\n",
+ "\n",
+ "Therefore, there's only one such object.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many objects are either \"tiny green things\" or \"shiny blocks\" that are in front of the gray rubber sphere.\n",
+ "\n",
+ "First, I need to identify all the objects that fit into each category separately.\n",
+ "\n",
+ "Looking at the scene:\n",
+ "\n",
+ "1. **Tiny green things**: The only object described as green is a small green cylinder. So there's 1.\n",
+ "\n",
+ "2. **Shiny blocks**: There's one large gray metal cube. Since the question mentions \"shiny\", we need to check if this cube is shiny. The material listed for the cube is \"metal\", which is a type of metal. Metals are typically considered shiny. Therefore, the cube is shiny. So this counts as 1.\n",
+ "\n",
+ "Now, both categories have one item each. Adding them together: 1 + 1 = 2.\n",
+ " 2 \n",
+ "predicted:2\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's tackle this question step by step. The user is asking if the number of green things in front of the big yellow thing is the same as the number of big cyan rubber objects.\n",
+ "\n",
+ "First, I need to parse the scene described. Let's list all the objects mentioned:\n",
+ "\n",
+ "1. A large blue rubber cube...\n",
+ "2. A small brown metal cube...\n",
+ "3. A large green rubber sphere...\n",
+ "4. A large purple metal sphere...\n",
+ "5. A large yellow metal sphere...\n",
+ "6. A small brown metal sphere...\n",
+ "7. A small green metal cylinder...\n",
+ "8. A large green metal sphere...\n",
+ "\n",
+ "Now, the key here is to identify which objects are \"green\" and \"big\", and then check their positions relative to the \"big yellow thing\". Additionally, we need to count the number of big cyan rubber objects.\n",
+ "\n",
+ "Looking at the objects, there are two green things: the first (large green rubber sphere) and the eighth (small green metal cylinder). So that's 2 green things.\n",
+ "\n",
+ "The \"big yellow thing\" refers to the fifth object: the large yellow metal sphere. Now, I need to determine its position. Since there's only one large yellow object, it's the fifth item. Are there any other objects in front of\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of large brown metallic blocks that are in front of a certain condition. First, I need to parse through the scene described.\n",
+ "\n",
+ "There are three objects: a small red rubber cylinder, a large brown metallic cube, and a large gray metallic cube. The key here is to identify which cubes are \"large\" and \"metallic,\" and then determine their positions relative to each other.\n",
+ "\n",
+ "Looking at the cubes:\n",
+ "\n",
+ "1. The first cube is large and brown, made of metal. Its coordinates are (2.73, 0.89, 0.56). The second cube is large and gray, metal. Coordinates are (3.43, -2.03, 0.75).\n",
+ "\n",
+ "The question mentions \"the large metal cube that is behind the shiny block on the right side of the tiny red object.\" Let's break this down step by step.\n",
+ "\n",
+ "First, identify the \"shiny block\" (which would be the large gray cube) and its position. The \"right side\" refers to the direction along the x-axis. So, if we look at the coordinates, the large gray cube is at (-2.03, -2.0\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The user provided a list of objects described in the scene. Each entry starts with \"A large green metal cylinder...\" followed by other attributes like color, size, rotation, coordinates, and pixel coordinates. There's also a mention of a small blue rubber cube and a large purple rubber sphere. \n",
+ "\n",
+ "First, I need to count each distinct objects listed here. Let me go through each line one by one.\n",
+ "\n",
+ "1. First object: A large green metal cylinder. That's one item.\n",
+ "2. Second is another large green metal cylinder. That's two items now.\n",
+ "3. Third is a large gray rubber cube. Third item.\n",
+ "4. Fourth is a small blue rubber cube. Fourth.\n",
+ "5. Fifth is a large purple rubber sphere. Fifth.\n",
+ "\n",
+ "So, counting them all together: 1, 2, 3, 4, 5. Therefore, there are five items in total.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 6 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small cyan metal cylinder...\" or \"A large purple metal sphere...\" etc. Each of these entries describes a separate object. There are six such entries mentioned. So that's six items. No duplicates mentioned here. Therefore, the total number should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let's tackle this question step by step. The user is asking how many objects meet certain criteria: either small cyan matte cubes or tiny cylinders that are to the right of a specific object.\n",
+ "\n",
+ "First, I need to parse through each object in the scene. Let me list them out:\n",
+ "\n",
+ "1. Large purple metal cylinder\n",
+ "2. Small purple metal sphere\n",
+ "3. Large blue rubber sphere\n",
+ "4. Large brown rubber sphere\n",
+ "5. Small yellow rubber cube\n",
+ "6. Small green rubber cylinder\n",
+ "7. Large cyan rubber cylinder\n",
+ "8. Small cyan rubber cylinder\n",
+ "\n",
+ "Now, the first part of the question is \"small cyan matte cubes.\" So, looking for cubes that are small, cyan, and matte. The only cube here is the small yellow one. But its color is yellow, not cyan. So there's no small cyan matte cube.\n",
+ "\n",
+ "Next, the second part is \"tiny cylinders that are to the right of the small green rubber cylinder.\" First, identify the small green rubber cylinder. In the list, the third item is a small green rubber cylinder. Now, we need to find all tiny cylinders (which are cylinders with small size) that are to the right of this cylinder. \n",
+ "\n",
+ "Looking at the coordinates, the small green cylinder is\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many other objects share the same material as the red cylinder. First, I need to identify the material of the red cylinder.\n",
+ "\n",
+ "Looking at the scene, there are three objects: a small green rubber cylinder and a small green metal sphere. The question is about the red cylinder. The red cylinder is made of rubber. So, I need to check if any other object in the scene has the same material.\n",
+ "\n",
+ "The other two objects are both green. The first one is a small green rubber cylinder, which matches the red cylinder's material. The second is a small green metal sphere. Since the red cylinder is rubber, the other green object is also rubber. Therefore, there should be 1 other object with the same material.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at each object listed one by one.\n",
+ "\n",
+ "First, there's a large purple rubber cylinder. That's one item. Next, a large cyan metal sphere. Second item. Then a small yellow rubber cube. Third is another large purple metal sphere. And lastly, a small purple rubber sphere. So that's four items in total. Each entry has distinct attributes like size, color, material, shape, rotation, coordinates, and pixel coordinates. Each of these entries represents a separate object. Therefore, the answer should be 4.\n",
+ " 4 \n",
+ "predicted:4\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let's tackle this question step by step. The user is asking if the number of cubes that are in front of the big metallic ball is greater than the number of yellow metal blocks that are in front of the rubber block.\n",
+ "\n",
+ "First, I need to parse through each object in the scene and determine their positions relative to the two specified objects: the big metallic ball and the rubber block.\n",
+ "\n",
+ "Starting with the big metallic ball. Looking at the scene, there's a large red metal sphere (coordinates: 3D (-2.70, -1.95, 0.86), pixel (141, 101, 16.00)). So the big metallic ball is located at that position.\n",
+ "\n",
+ "Next, the rubber block. There's a small blue rubber cube (coordinates: 3D (2.93, -1.00, 0.32), pixel (332, 199, 16.00)). So the rubber block is here.\n",
+ "\n",
+ "Now, we need to figure out which cubes are in front of the big metallic ball. To do this, I'll use the camera's perspective. Assuming the camera is looking along the positive Z-axis\n",
+ "predicted:None\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same color as the small matte cylinder. First, I need to identify the color of the small matte cylinder in the scene.\n",
+ "\n",
+ "Looking at the image, there's a \"small purple rubber cylinder\" mentioned. So the color here is purple. Now, I need to check all other objects to see if any have the same color.\n",
+ "\n",
+ "The first object is a large yellow rubber cube. That's not purple. The second one is a small brown rubber sphere, which is brown. The third is a small gray metal cube. None of these three objects are purple. Therefore, there are zero other objects with the same color as the small matte cylinder.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same color as the \"tiny cylinder\". First, I need to identify the color of the tiny cylinder in the scene.\n",
+ "\n",
+ "Looking through the list, there's a \"small cyan rubber cylinder\" mentioned. So the color here is cyan. Now, I need to check all other objects to see if any have the same color.\n",
+ "\n",
+ "Let me go through each object one by one:\n",
+ "\n",
+ "1. The first object is a large green rubber cylinder. That's green, not cyan. So this doesn't match.\n",
+ "2. The second is a small gray rubber cube. Gray isn't cyan.\n",
+ "3. Third is a large blue metal sphere. Blue again, different from cyan.\n",
+ "4. Fourth is a small red rubber sphere. Red, no.\n",
+ "5. Fifth is the tiny cyan rubber cylinder itself. Wait, but the question asks for \"other objects\", so we don't count this one.\n",
+ "\n",
+ "So, checking the remaining four objects: none of them are cyan. Therefore, there are zero other objects with the same color as the tiny cylinder.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user provided a list of objects with their properties. Each entry starts with \"A small gray metal cube...\" followed by other attributes like color, size, rotation, coordinates, and pixel coordinates. There's also a large cyan rubber cylinder, a small red metal cylinder, a small purple metal cylinder, a small brown rubber sphere, a large blue rubber cube, and another small cyan rubber cylinder. \n",
+ "\n",
+ "Each entry is a separate object. So each item has its own distinct characteristics. Let me count them one by one.\n",
+ "\n",
+ "First: A small gray metal cube. That's one. Second, a large cyan rubber cylinder. Second. Third, small red metal cylinder. Third. Fourth, small brown rubber sphere. Fourth. Fifth, large blue rubber cube. Fifth. Sixth, small cyan rubber cylinder. So that's six items total. Therefore, the answer should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of rubber objects that are either big green balls or big yellow cubes. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to check each item in the scene. Each entry has attributes like color, size, shape, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Starting with the first object: a large purple metal cylinder. It's not a rubber material, so no.\n",
+ "\n",
+ "Second, a small gray rubber cube. That's a rubber cube, but it's small, so doesn't qualify. So this is zero.\n",
+ "\n",
+ "Third, a large green metal sphere. This is a metal sphere, so not a rubber one. So still zero.\n",
+ "\n",
+ "Fourth, a small red rubber cylinder. Again, not a ball or cube. So zero again.\n",
+ "\n",
+ "Fifth, a large yellow metal cube. This is a cube, but it's yellow, which is not green. So no.\n",
+ "\n",
+ "Sixth, a large cyan rubber sphere. This is a rubber sphere, but it's cyan, not green. So zero.\n",
+ "\n",
+ "Seventh, a large green metal sphere. Same as the third one, not a rubber object. Zero.\n",
+ "\n",
+ "Eighth, a small gray rubber cube. Another rubber\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other shiny objects share the same shape as the \"large brown shiny cube\". First, I need to identify all the objects in the scene that are shiny and then check if any of them are cubes.\n",
+ "\n",
+ "Looking at each object one by one:\n",
+ "\n",
+ "1. The first object is a small red metal cylinder. It's made of metal, so it's shiny. But its shape is a cylinder, not a cube. So this doesn't count.\n",
+ "\n",
+ "2. The second object is a small cyan metal cylinder. Again, it's shiny but has a different shape (cylinder). So no.\n",
+ "\n",
+ "3. The third object is a large brown metal cube. This one is a cube, which matches the target shape. Since there are no other cubes mentioned in the scene, this is the only one that fits.\n",
+ "\n",
+ "So, since there's only one cube among the three objects, the answer should be 0.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of tiny yellow metal objects that are to the left of a specific object. First, I need to parse through all the objects listed.\n",
+ "\n",
+ "Looking at each item:\n",
+ "\n",
+ "1. A small cyan rubber cylinder rotated 235.7° located at certain coordinates.\n",
+ "2. A large red rubber sphere at different coordinates.\n",
+ "3. A small red rubber cylinder with its own rotation and coordinates.\n",
+ "4. A small gray rubber cube with rotation and coordinates.\n",
+ "5. A small yellow metal cube with rotation and coordinates.\n",
+ "6. A small blue metal cube with rotation and coordinates.\n",
+ "\n",
+ "The key here is identifying \"tiny yellow metal objects\" and their positions relative to the specified object. Let's break down the conditions step by step.\n",
+ "\n",
+ "First, identify the \"tiny blue object that is in front of the red rubber thing behind the big cyan cylinder\". \n",
+ "\n",
+ "Wait, wait. The question mentions \"the red rubber thing behind the big cyan cylinder\". So first, find the red rubber sphere (which is the \"red rubber thing\") and then look for the big cyan cylinder. Then, any object in front of both of these needs to be considered.\n",
+ "\n",
+ "Let's start with the red rubber sphere: The red rubber sphere is\n",
+ "predicted:None\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user provided a list of objects with their properties. Each entry starts with \"A small brown rubber cube...\" followed by other attributes like color, size, shape, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "First, I need to count each distinct object listed here. Let's go through each line one by one.\n",
+ "\n",
+ "1. A large yellow rubber cube: That's one item.\n",
+ "2. A small purple metal cylinder: Second item.\n",
+ "3. A large red metal sphere: Third.\n",
+ "4. A small blue rubber sphere: Fourth.\n",
+ "5. A small green rubber cylinder: Fifth.\n",
+ "6. A small brown rubber cube: Sixth.\n",
+ "7. A large red metal sphere: Seventh.\n",
+ "\n",
+ "Each of these entries is a separate object. There are seven distinct items described. So the total number should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user provided a list of objects with their properties. Each entry starts with \"A small cyan metal cylinder...\" followed by its attributes. Let's count each item one by one.\n",
+ "\n",
+ "First object: A small cyan metal cylinder. That's one item. Next is another small cyan metal cylinder. That's two items. Then a large brown rubber cube. Third is a small gray rubber sphere. Fourth is a large purple metal cylinder. Fifth is a large gray metal cube. So that's five items in total. The sixth is a small blue rubber sphere. Wait, but wait, the question asks for the number of items, not types. So each entry is a separate item regardless of color or material. Therefore, the answer should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many shiny blocks are located on the right side of the large red rubber cylinder and behind the small gray metal cube.\n",
+ "\n",
+ "First, I need to identify all the objects in the scene. Let's list them out:\n",
+ "\n",
+ "1. A large blue metal cube rotated 235.4° at (-0.79, -1.68, 0.75) with size large, color blue, material metal, rotation 235.4°, coordinates (x: -0.79, y: -1.68, z: 0.75).\n",
+ "2. A small green metal cylinder rotated 162.2° at (1.33, -2.30, 0.35), size small, color green, material metal, rotation 162.2°, coordinates (1.33, -2.30, 0.35).\n",
+ "3. A large red rubber cylinder rotated 29.0° at (2.76, 0.73, 0.75), size large, color red, material rubber, rotation\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small green rubber cylinder...\" or \"A large brown rubber sphere...\" followed by their attributes like color, size, rotation, coordinates, and pixel coordinates. \n",
+ "\n",
+ "Let's count them up. There are seven entries here. Each entry describes a different object. So that's 7 items. No duplicates mentioned. Therefore, the total number should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small cyan metal cylinder...\" or \"A large gray metal cube...\" followed by coordinates and rotation. \n",
+ "\n",
+ "Starting with the first line: A small cyan metal cylinder rotated 162.3°. That's one item. Next, a small gray metal sphere. Second item. Third is another small cyan metal cylinder. Fourth is a small gray metal cube. Fifth is a large gray metal cube. Sixth is a small blue rubber cube. Seventh is a large cyan metal cylinder. So that's six items total. There are no other entries mentioned. Therefore, the answer should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many brown objects share the same shape as the large purple cylinder. First, I need to identify all the objects in the scene that are brown and then check their shapes.\n",
+ "\n",
+ "Looking at the image, there are two brown objects mentioned: a large brown rubber cube and another large brown rubber cube. The large purple object is a cylinder. So, I need to compare these two brown objects with the cylinder.\n",
+ "\n",
+ "The first brown object is a cube. The second one is also a cube. The third object is a cylinder. Since both cubes are the same shape (cube) and the cylinder is a different shape, the answer should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many small things are either blue rubber objects or purple metallic blocks. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to identify which objects meet both criteria: being small, and either being a blue rubber object or a purple metallic block.\n",
+ "\n",
+ "Looking at the first object: a small blue metal sphere. It's small (since its size is \"small\") and it's made of metal, not rubber. So this doesn't fit either category. So that's zero.\n",
+ "\n",
+ "Second object: a large blue rubber cube. This is large, so it doesn't qualify as small. Also, it's rubber, but the color is blue. So no.\n",
+ "\n",
+ "Third object: a large gray rubber sphere. Large and rubber, so not small. Color is gray, so not blue. Therefore, this also doesn't count.\n",
+ "\n",
+ "So adding them up, there are zero small objects that are either blue rubber or purple metallic. Since none of the objects meet both conditions, the answer should be zero.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking for the number of big matte cylinders that share the same color as the red metal sphere. First, I need to identify all objects in the scene that are both large, made of rubber (matte), and have the same color as the red metal sphere.\n",
+ "\n",
+ "Looking at the image, there are three objects: a small yellow rubber sphere, a large green rubber cylinder, and a large red metal sphere. \n",
+ "\n",
+ "The red metal sphere is described as \"large\" and made of metal. So we're looking for a large, matte (rubber) cylinder with the same color - red. \n",
+ "\n",
+ "In the list, the only large object is the third one, which is a large green rubber cylinder. However, its color is green, not red. Therefore, there are zero such objects.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are more brown cubes than small gray blocks in the scene. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to count the number of brown cubes. Looking at the image, there are two cubes mentioned: a large brown metal cube and another large brown metal cube. So that's two brown cubes.\n",
+ "\n",
+ "Next, for the small gray block. There's only one small gray cube here. So the count is 2 brown cubes and 1 gray cube. Therefore, the answer should be yes, because 2 is greater than 1.\n",
+ " yes \n",
+ "predicted:yes\n",
+ "groundtruth:[' yes ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many things are either small rubber balls or tiny gray things. Let me go through each item one by one.\n",
+ "\n",
+ "First, I need to check if any object is a small rubber ball. Looking at the list:\n",
+ "\n",
+ "1. A large purple metal cylinder – not a ball, so no.\n",
+ "2. A small red metal cylinder – also not a ball.\n",
+ "3. A small cyan rubber sphere – that's a sphere, but it's cyan, not purple. So this doesn't count.\n",
+ "4. A large blue metal sphere – again, not a small rubber ball.\n",
+ "5. A small purple rubber sphere – this one fits both categories: small and rubber. So that's 1.\n",
+ "6. A small gray rubber cube – yes, this is a small gray thing. So that's another 1.\n",
+ "7. A small cyan rubber cylinder – not a small gray thing.\n",
+ "8. A large purple metal cylinder – not relevant here.\n",
+ "9. A small purple metal cylinder – same as the first one.\n",
+ "\n",
+ "So adding up the two counts (1 + 1) gives us 2. Therefore, there are 2 things that meet either category.\n",
+ " 2 \n",
+ "predicted:2\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many small objects are either spheres or green matte cylinders. Let me go through each object one by one.\n",
+ "\n",
+ "First, I need to identify all the small objects in the scene. The user listed several items: a large gray metal cube, a large red rubber cylinder, a small purple rubber cylinder, a small yellow rubber cube, a small purple metal sphere, and a large cyan rubber cylinder. \n",
+ "\n",
+ "Looking at the first part of the question: \"small objects\". So, any object that is small. The small objects here are the small purple rubber cylinder and the small purple metal sphere. That's two small objects.\n",
+ "\n",
+ "Now, the second part is \"matte cylinders\". Let's check each small object:\n",
+ "\n",
+ "1. The small purple rubber cylinder is matte (since it's rubber), but it's not green. So this doesn given condition.\n",
+ "2. The small purple metal sphere is small, but it's made of metal, so not matte. Also, it's a sphere, not a cylinder. So this doesn't count.\n",
+ "\n",
+ "So, only the small purple rubber cylinder meets both conditions: small and matte. Therefore, there's 1 such object.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many small red cylinders are there in the scene. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to look through all the objects listed here. Each entry has attributes like color, size, shape, rotation, 3D coordinates, and pixel coordinates. \n",
+ "\n",
+ "Looking at each item:\n",
+ "\n",
+ "1. A large red metal cylinder: This is a cylinder, but its color is red, so that's one. But the question asks for \"small\" red cylinders. So this doesn't count.\n",
+ "2. Another small red metal cylinder: This one is also a cylinder, but it's small, so that's another one.\n",
+ "3. A small purple rubber cylinder: Not red, so no.\n",
+ "4. Small brown metal cube: Cube, not a cylinder.\n",
+ "5. Large gray rubber cube: Cube again, not a cylinder.\n",
+ "6. Small blue metal sphere: Sphere, not a cylinder.\n",
+ "7. Small red metal cylinder: Yes, this one is both small and red. That's the third one.\n",
+ "\n",
+ "So, counting them up, there are two small red cylinders. Therefore, the answer should be 2.\n",
+ " 2 \n",
+ "predicted:2\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The user provided a scene with several objects. I need to count them one by one.\n",
+ "\n",
+ "First, I'll go through each item listed here. Let me list them out:\n",
+ "\n",
+ "1. A small purple rubber cylinder rotated 235.7° located at certain coordinates.\n",
+ "2. A large green rubber cylinder, also rotated 240.9°.\n",
+ "3. A small cyan rubber sphere.\n",
+ "4. A large purple rubber cylinder.\n",
+ "5. A small red rubber cylinder.\n",
+ "6. A small purple metal cylinder.\n",
+ "\n",
+ "Each of these entries is a separate object. There are six distinct items described. So the total number should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are the same number of small green cylinders and blue metallic balls in the scene. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to look at each item listed and determine their attributes. \n",
+ "\n",
+ "Starting with the first object: A small yellow rubber cube. Its color is yellow, material is rubber, size is small. So that's not relevant here.\n",
+ "\n",
+ "Second, a large cyan rubber cylinder. Color is cyan, so not green or blue. Size is large. So this doesn't count towards either category.\n",
+ "\n",
+ "Third, a small red rubber cylinder. This is a cylinder but its color is red, which isn't green or blue. So no.\n",
+ "\n",
+ "Fourth, a small green metal cylinder. This is a cylinder, but its color is green. So that's one for green cylinders. But the question asks for both small green cylinders and blue metallic balls. Since there's only one green cylinder, and no mention of any blue metallic balls, they don't match.\n",
+ "\n",
+ "Therefore, the answer should be no, because there's one green cylinder and zero blue metallic balls.\n",
+ " no \n",
+ "predicted:no\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many things are either red shiny cylinders or red matte objects. Let me go through each object one by one.\n",
+ "\n",
+ "First, there's a small cyan rubber cylinder. Its color is cyan, so that doesn't fit either category. Next, a small gray rubber cube. Again, gray, not red. So that's two non-red objects.\n",
+ "\n",
+ "Now, the third object is a small red metal cylinder. This one is red and made of metal. Since it's red and shiny (metal), it fits the first part of the question. But wait, the question says \"either... or\". So even if this one is red and shiny, it still counts as a red shiny cylinder. Therefore, that's one more.\n",
+ "\n",
+ "So in total, there's 1 red shiny cylinder. The other objects don't meet either condition. So the answer should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at the image provided.\n",
+ "\n",
+ "First, I need to count each object listed here. Each entry starts with \"A small brown metal cube...\" followed by its attributes. There are three such entries: one large yellow rubber cube, one small blue rubber cube, and another small brown metal cube. That's three distinct objects. So the answer should be 3.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 4 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many spheres are either shiny or rubber. Let me look at each object one by one.\n",
+ "\n",
+ "First, I need to check each object in the scene for both categories: \"shiny\" and \"rubber\". \n",
+ "\n",
+ "Looking at the first object: A large purple rubber cylinder. It's described as \"large\", \"purple\", \"rubber\", and \"rotated 163.5°\". Since it's a cylinder, which is a shape, not a sphere, so it doesn't count towards the count. So that's zero.\n",
+ "\n",
+ "Second object: a small brown metal cube. This is a cube, so again, not a sphere. So no contribution here.\n",
+ "\n",
+ "Third object: a large red metal cylinder. Cylinder again, so no. Therefore, zero.\n",
+ "\n",
+ "So adding them up, there are zero objects that are either shiny (metal) or rubber (rubber). The answer should be zero.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same material as the small purple rubber cylinder. First, I need to identify the material of that object.\n",
+ "\n",
+ "Looking at the image, the small purple rubber cylinder has the following attributes: size small, color purple, material rubber. So its material is rubber.\n",
+ "\n",
+ "Now, I'll check each other object to see if they're also made of rubber. Let me go through each one:\n",
+ "\n",
+ "1. The large green metal cylinder: Material is metal. Different from rubber.\n",
+ "2. Large red rubber cylinder: Yes, this one is also rubber.\n",
+ "3. Small purple rubber cylinder: This one matches the target object.\n",
+ "4. Small purple rubber cylinder: Same material as the first one.\n",
+ "5. Large green metal cylinder: Metal again.\n",
+ "6. Large gray rubber sphere: Rubber.\n",
+ "7. Small purple rubber cylinder: Again, rubber.\n",
+ "8. Large purple rubber cylinder: Rubber.\n",
+ "\n",
+ "So, counting all the cylinders that are rubber, there are three: two small purple ones and two large ones. The small gray sphere is not a cylinder, so it doesn given in the question. Therefore, the answer should be 3.\n",
+ " 3 \n",
+ "predicted:3\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking if there are an equal number of large cylinders and tiny green rubber spheres in the scene. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to look at the first object: a \"large red rubber cylinder\". The size here is \"large\" and color is red. So this is a cylinder. Now, the second object is a \"small purple rubber cylinder\". The size here is small, so that's different from the first one. The material is rubber, but the color is purple, not green. So these two objects have different sizes and colors. Therefore, they don't match either in terms of size or color. \n",
+ "\n",
+ "Next, the third object is a \"large blue metal cube\". This is a cube, which is a different shape. Even though it's large, its color is blue and material is metal, so it doesn't fit either category. \n",
+ "\n",
+ "So comparing them: 1 large cylinder vs. 0 tiny green rubber spheres. Since there are zero tiny green rubber spheres mentioned, the answer should be no.\n",
+ " no \n",
+ "predicted:no\n",
+ "groundtruth:[' no ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user provided a list of objects with their properties. Each entry starts with \"A small purple rubber cylinder...\" followed by its attributes. Let's count them one by one.\n",
+ "\n",
+ "First item: A small purple rubber cylinder. That's one object. Next is another small purple rubber cylinder. That's two. Third is a large yellow metal cube. Fourth is a large cyan metal sphere. Fifth is a large red metal cylinder. Sixth is a large brown rubber cube. Seventh is a large cyan rubber cylinder. So that's six items listed here. There are no other entries mentioned. Therefore, the total number should be 6.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's go through each item listed one by one.\n",
+ "\n",
+ "First, I'll count each object mentioned. Each entry starts with \"A small brown rubber cube...\" or similar. There are seven entries here. Each has distinct attributes like color, size, shape, rotation, coordinates, and pixel coordinates. Since each of these entries represents a separate object, that makes seven items total. So the answer should be 7.\n",
+ " 7 \n",
+ "predicted:7\n",
+ "groundtruth:[' 10 ']\n",
+ "\n",
+ "Okay, let's tackle this question step by step. The user is asking for the number of green objects that share the same material as the big yellow sphere. First, I need to identify which object is the \"big yellow sphere\" in the scene.\n",
+ "\n",
+ "Looking through the list, there's a \"large yellow rubber sphere\" mentioned. So the big yellow sphere here refers to the large yellow rubber sphere. Now, I need to check each object to see if its material matches that of the yellow sphere.\n",
+ "\n",
+ "Let's go through each item one by one:\n",
+ "\n",
+ "1. **Large blue metal sphere**: Material is metal. Not yellow or rubber, so doesn't match.\n",
+ "2. **Small red metal cylinder**: Metal again, not yellow or rubber.\n",
+ "3. **Large purple metal cylinder**: Purple, not yellow.\n",
+ "4. **Large gray rubber cube**: Rubber, but not yellow.\n",
+ "5. **Small gray metal cube**: Metal, no.\n",
+ "6. **Large gray rubber cube**: Rubber, but not yellow.\n",
+ "7. **Large purple rubber sphere**: Same material as the yellow sphere (rubber). This one matches.\n",
+ "8. **Small blue rubber cube**: Rubber, but not yellow.\n",
+ "9. **Large blue metal cube**: Metal, not yellow.\n",
+ "10\n",
+ "predicted:None\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same color as the mentioned object. First, I need to identify which object is red. Looking at the scene, there's a \"small yellow rubber cube\" and a \"small red rubber sphere\". The cube is yellow, so we're looking for another red object.\n",
+ "\n",
+ "The question mentions a \"small red rubber cylinder\". So, I need to check if there's any other red object besides the small red rubber sphere. Let me list all the objects again: a small yellow cube and a small red sphere. Since both are red, but they are different shapes (cube vs. sphere), there's only one other object that shares the same color. Therefore, the answer should be 1.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 0 ']\n",
+ "\n",
+ "Okay, let's try to figure this out step by step. The question is asking how many things are either small yellow objects or big objects to the right of the tiny purple rubber cylinder.\n",
+ "\n",
+ "First, I need to identify all the objects in the scene and their attributes. Let me list them out:\n",
+ "\n",
+ "1. A large blue rubber cube (large object)\n",
+ "2. Small purple rubber cylinder (small object)\n",
+ "3. Small yellow rubber cube (small object)\n",
+ "4. Large red metal sphere (large object)\n",
+ "5. Small purple metal cylinder (small object)\n",
+ "\n",
+ "Now, the key here is to find objects that are either small yellow or big objects to the right of the tiny purple rubber cylinder. First, I need to determine which objects are small yellow. Looking through the list, there's one small yellow object: the third item. Next, I need to check if any other objects are large and located to the right of the purple rubber cylinder. The purple rubber cylinder is at coordinates (-0.79, 0.68, 0.35). So, I need to check each large object's position relative to that point.\n",
+ "\n",
+ "Looking at the large objects: the first and fourth items. The first is a large blue cube at (-2.\n",
+ "predicted:None\n",
+ "groundtruth:[' 2 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many other objects share the same material as the purple rubber sphere. First, I need to identify the material of the purple rubber sphere. Looking at the scene, the first object described is a \"small purple rubber sphere\". So its material is rubber.\n",
+ "\n",
+ "Now, I need to check all other objects to see if any have the same material. Let me list them out again:\n",
+ "\n",
+ "1. Small cyan metal cylinder: Material is metal (cylinder)\n",
+ "2. Large brown metal sphere: metal\n",
+ "3. Large green rubber cylinder: rubber\n",
+ "4. Small gray metal cube: metal\n",
+ "\n",
+ "The question is about the number of objects with the same material as the purple rubber sphere. Since the sphere is rubber, I need to count how many other objects are also made of rubber. The only other object that matches this is the large green rubber cylinder. Therefore, there should be one such object.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 1 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small cyan rubber cylinder...\" or \"A large brown rubber sphere...\" or similar. There's also a \"large blue metal cube\" and two small spheres. So that's five distinct objects. Each of these entries seems to be separate entities. Even though some might have similar attributes like color or material, they're different shapes and sizes. Therefore, each counts as a separate item. So total should be 5.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 6 ']\n",
+ "\n",
+ "Okay, let's see. The user provided a list of objects with their properties. Each entry starts with \"A small red metal sphere...\" followed by other attributes like color, size, rotation, coordinates, and pixel coordinates. There are six such entries listed here. Each entry describes a separate object. So each is a distinct item. Therefore, counting them should be straightforward. Let me count them one by one.\n",
+ "\n",
+ "First: A small red metal sphere. That's one. Second: A large brown metal sphere. That's two. Third: A small yellow rubber cube. Three. Fourth: A large purple metal cylinder. Four. Fifth: A small gray rubber sphere. Five. Sixth: A large red rubber cylinder. Six. \n",
+ "\n",
+ "So, adding these up: 1 + 2 + 3 + 4 + 5 + 6 = 17. Therefore, there are six items in total.\n",
+ " 6 \n",
+ "predicted:6\n",
+ "groundtruth:[' 8 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many balls are either gray objects or brown rubber objects. Let me check each object one by one.\n",
+ "\n",
+ "First, I need to look at each item in the scene provided. There are four objects listed here: a large purple metal cylinder, a small blue metal cube, a small blue metal cube, and a large brown rubber cube.\n",
+ "\n",
+ "Looking at the first object: \"A large purple metal cylinder\". This is a cylinder, not a ball, so it doesn't count. Next, the second is a small blue metal cube. Again, that's a cube, not a ball. Third is another cube, but smaller and blue. So that's two non-brown or non-gray objects.\n",
+ "\n",
+ "Now, the fourth object is a large brown rubber cube. This one is both brown (which is a color) and made of rubber (rubber). Since it's a cube, it's a solid shape, so it's not a ball. Therefore, this counts as a brown rubber object. \n",
+ "\n",
+ "So adding up all the符合条件的 items: 1 (brown rubber) + 0 (other colors) = 1. Therefore, there's 1 such object.\n",
+ " 1 0 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the scene described. Let's look at each item listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A\" followed by attributes like color, size, shape, rotation, coordinates, and pixel coordinates. \n",
+ "\n",
+ "Starting with the first line: A small yellow rubber cube. That's one item. Next, a large red metal sphere. Second item. Then a large purple metal cylinder. Third is another large purple rubber cylinder. Fourth is a small purple rubber cylinder. Fifth is a large yellow metal cube. Sixth is a small gray rubber sphere. Seventh is a large red rubber cylinder. Eighth is a small yellow metal cube. So that's eight items total. Each entry is a separate object. There are no duplicates mentioned here. Therefore, the answer should be 8.\n",
+ " 8 \n",
+ "predicted:8\n",
+ "groundtruth:[' 9 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects have the same size as the blue matte block. First, I need to identify the blue matte block in the scene.\n",
+ "\n",
+ "Looking through the list, there's a \"small blue rubber cube\" mentioned. So that's one object. Now, I need to check all others to see if any are also small and made of rubber.\n",
+ "\n",
+ "Let me go through each item:\n",
+ "\n",
+ "1. Small green metal cylinder: Not the same size or material.\n",
+ "2. Small purple rubber cylinder: Same size but different color and material.\n",
+ "3. Large gray metal cube: Different size.\n",
+ "4. Small purple metal cylinder: Different size.\n",
+ "5. Small green metal cylinder: Different size.\n",
+ "6. Large green rubber cylinder: Different size.\n",
+ "7. Small blue rubber sphere: Same size and material as the block (both small and rubber).\n",
+ "\n",
+ "So, only the blue rubber sphere matches the blue matte block. Therefore, there's 1 other object with the same size.\n",
+ " 1 \n",
+ "predicted:1\n",
+ "groundtruth:[' 6 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same size as the purple cylinder. First, I need to identify all the cylinders in the scene.\n",
+ "\n",
+ "Looking through each object listed:\n",
+ "\n",
+ "1. A large cyan rubber sphere (large)\n",
+ "2. Small red metal cube (small)\n",
+ "3. Large gray metal cube (large)\n",
+ "4. Large cyan rubber sphere (large)\n",
+ "5. Small red metal cylinder (small)\n",
+ "6. Large purple metal cylinder (large)\n",
+ "\n",
+ "The purple cylinder is the first item mentioned. So, I need to count how many other objects are also large. Let me check each one again:\n",
+ "\n",
+ "- The second object is small, so not the same size.\n",
+ "- Third is large, which matches.\n",
+ "- Fourth is large, same as the purple one.\n",
+ "- Fifth is small, so no.\n",
+ "- Sixth is large, same as the purple one.\n",
+ "\n",
+ "So there are two other objects that are the same size as the purple cylinder: the third and fourth ones.\n",
+ " 2 \n",
+ "predicted:2\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects share the same material as the \"big yellow metal sphere\". First, I need to identify which object is made of the same material as the described one.\n",
+ "\n",
+ "Looking at the scene, there's a \"large yellow metal sphere\" mentioned. Let me check each object one by one.\n",
+ "\n",
+ "First object: A small red rubber sphere. Material here is rubber, so that doesn't match. Next, the large yellow metal sphere. That matches the color and material. So this is the one we're comparing against. Now, the third object is a large green metal cylinder. Its material is metal, which is different from the yellow metal sphere. The fourth is a small red metal cylinder, also metal. So both these two have the same material as the first object. Therefore, there should be 2 such objects.\n",
+ " 2 \n",
+ "predicted:2\n",
+ "groundtruth:[' 3 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many items are there in the described scene. Let me look at each object listed one by one.\n",
+ "\n",
+ "First, there's a large brown rubber cube. That's one item. Next, a large yellow metal cube. That's two items. Then a small cyan rubber cylinder. Third, a large green rubber cylinder. Fourth, a large purple rubber sphere. Fifth, another large brown rubber cube. So that's five items in total. Each entry has distinct attributes like color, size, material, shape, rotation, 3D coordinates, and pixel coordinates. Each of these entries represents a separate object. Therefore, the answer should be five.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 5 ']\n",
+ "\n",
+ "Okay, let me try to figure this out. The user is asking how many items are there in the described scene. Let's look at each object listed one by one.\n",
+ "\n",
+ "First, I'll go through each entry. Each entry starts with \"A small cyan metal cylinder...\" or \"A large brown rubber cube...\" etc. Each of these entries describes a separate object. There are five such entries here. So that's five objects. No duplicates mentioned. Therefore, the total number should be 5.\n",
+ " 5 \n",
+ "predicted:5\n",
+ "groundtruth:[' 7 ']\n",
+ "\n",
+ "Okay, let's see. The user is asking how many other objects share the same material as the purple metal sphere. First, I need to identify which object is made of the same material as the purple metal sphere.\n",
+ "\n",
+ "Looking at the scene, there's one purple object mentioned: a small purple metal sphere. Now, I need to check all other objects to see if any others are also made of metal. Let me list them out:\n",
+ "\n",
+ "1. A large green rubber sphere - material: rubber\n",
+ "2. Large red metal cylinder - metal\n",
+ "3. Small purple metal sphere - yes, this one matches\n",
+ "4. Small green rubber sphere - rubber\n",
+ "5. Large yellow metal cube - metal\n",
+ "\n",
+ "So, the question is asking for other objects that are made of the same material as the purple metal sphere. Since the purple metal sphere is already listed, we don't count it again. So the answer should be 0 because there are no other objects in the scene that are made of metal.\n",
+ " 0 \n",
+ "predicted:0\n",
+ "groundtruth:[' 3 ']\n"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 2243\u001b[0m \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2244\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2245\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 2246\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2247\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth_zoo/compiler.py\u001b[0m in \u001b[0;36m_fast_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/models/_utils.py\u001b[0m in \u001b[0;36m_unsloth_training_step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m 2246\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlomo_backward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2247\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2248\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2250\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mset_trigger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 624\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 625\u001b[0m )\n\u001b[0;32m--> 626\u001b[0;31m torch.autograd.backward(\n\u001b[0m\u001b[1;32m 627\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 628\u001b[0m )\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 345\u001b[0m \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m _engine_run_backward(\n\u001b[0m\u001b[1;32m 348\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 349\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py\u001b[0m in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 821\u001b[0m \u001b[0munregister_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_register_logging_hooks_on_whole_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 822\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 823\u001b[0;31m return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m 824\u001b[0m \u001b[0mt_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 825\u001b[0m ) # Calls into the C++ engine to run the backward pass\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2vo-QiybOi--",
+ "metadata": {
+ "id": "2vo-QiybOi--"
+ },
+ "source": [
+ "You can now save the LoRa weights"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "-f8XeizXOlUu",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-f8XeizXOlUu",
+ "outputId": "0cebbcd6-4d16-46d1-9787-5e16f50d5c5f"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.save_pretrained(\"vlmgrpo_save\") # Local saving\n",
+ "tokenizer.save_pretrained(\"vlmgrpo_save\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1iwDtnmb1lMQ",
+ "metadata": {
+ "id": "1iwDtnmb1lMQ"
+ },
+ "source": [
+ "Now let's test the result by taking a sample image from the dataset.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "RLvR0NXn3SBQ",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 337
+ },
+ "id": "RLvR0NXn3SBQ",
+ "outputId": "3f12629f-b774-4842-c532-5d1ec9f7fc96"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "image = dataset[10000]['image']\n",
+ "display(image)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "t3ZYVJw83UbR",
+ "metadata": {
+ "id": "t3ZYVJw83UbR"
+ },
+ "source": [
+ "Ok let's ask how many other objects are the same size as the grey sphere (answer : 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "id": "fhm7p6-C1rCc",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fhm7p6-C1rCc",
+ "outputId": "2c81721d-d962-4028-af0c-8098a941b6ad"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<|im_start|>system\n",
+ "You are given a problem with an image.\n",
+ " Think about the problem and provide your working out.\n",
+ " Place it between and .\n",
+ " Then, provide your solution between <|im_end|>\n",
+ "<|im_start|>user\n",
+ "<|vision_start|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|vision_end|>How many other objects are the same size as the grey sphere?<|im_end|>\n",
+ "<|im_start|>assistant\n",
+ "\n",
+ "Okay, let's see. The question is asking how many other objects have the same size as the grey sphere. First, I need to identify which object is the grey sphere. Looking at the image, there's a \"large gray rubber sphere\" mentioned. So that's our reference point.\n",
+ "\n",
+ "Next, I'll check all the other objects to see if any match its size. Let me list them one by one:\n",
+ "\n",
+ "1. Small brown metal cube – size: small\n",
+ "2. Large yellow metal cube – size: large\n",
+ "3. Small purple metal cylinder – size: small\n",
+ "4. Small blue metal sphere – size: small\n",
+ "5. Large gray rubber sphere (reference)\n",
+ "\n",
+ "Comparing each of these to the grey sphere (large). The large yellow cube and the large gray rubber sphere are both large. Therefore, two objects share the same size.\n",
+ "\n",
+ "So the answer should be 2.\n",
+ " 2 <|im_end|>\n"
+ ]
+ }
+ ],
+ "source": [
+ "question = 'How many other objects are the same size as the grey sphere?'\n",
+ "\n",
+ "message = [\n",
+ " {\"role\":\"system\",\n",
+ " \"content\": f\"\"\"You are given a problem with an image.\n",
+ " Think about the problem and provide your working out.\n",
+ " Place it between {reasoning_start} and {reasoning_end}.\n",
+ " Then, provide your solution between {solution_start}{solution_end}\"\"\"\n",
+ " },\n",
+ " {\"role\": \"user\",\n",
+ " \"content\": [\n",
+ " {\"type\": \"image\"},\n",
+ " {\"type\":\"text\",\"text\":question},\n",
+ " ]}]\n",
+ "\n",
+ "\n",
+ "input_text = tokenizer.apply_chat_template(message, add_generation_prompt=True)\n",
+ "inputs = tokenizer(\n",
+ " image,\n",
+ " input_text,\n",
+ " add_special_tokens=False,\n",
+ " return_tensors=\"pt\",\n",
+ ").to(\"cuda\")\n",
+ "\n",
+ "\n",
+ "from transformers import TextStreamer\n",
+ "_ = model.generate(\n",
+ " **inputs,\n",
+ " temperature = 0.5,\n",
+ " max_new_tokens = 1024,\n",
+ " streamer = TextStreamer(tokenizer, skip_prompt = False),\n",
+ ")\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "pCMwVIhu8gYZ",
+ "metadata": {
+ "id": "pCMwVIhu8gYZ"
+ },
+ "source": [
+ "Let's try an inference with the LoRa adpaters disabled to compare"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "id": "PJkzUo133ph9",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "PJkzUo133ph9",
+ "outputId": "72e57193-1beb-4bbe-8451-a6267348c174"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<|im_start|>system\n",
+ "You are given a problem with an image.\n",
+ " Think about the problem and provide your working out.\n",
+ " Place it between and .\n",
+ " Then, provide your solution between <|im_end|>\n",
+ "<|im_start|>user\n",
+ "<|vision_start|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|vision_end|>How many other objects are the same size as the grey sphere?<|im_end|>\n",
+ "<|im_start|>assistant\n",
+ "\n",
+ "The yellow cube is larger than the purple cylinder. The purple cylinder is larger than the blue sphere. The blue sphere is larger than the brown cube. The brown cube is larger than the grey cube.\n",
+ "\n",
+ "\n",
+ "2\n",
+ "<|im_end|>\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import TextStreamer\n",
+ "with model.disable_adapter():\n",
+ " _ = model.generate(\n",
+ " **inputs,\n",
+ " temperature = 0.1,\n",
+ " max_new_tokens = 1024,\n",
+ " streamer = TextStreamer(tokenizer, skip_prompt = False),\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3NlKPRZ63IQo",
+ "metadata": {
+ "id": "3NlKPRZ63IQo"
+ },
+ "source": [
+ "We can observe that the GRPO-trained VLM produces answers that are significantly more detailed and precise compared to the model without GRPO training — although it mistakenly counted the gray sphere along with the yellow cube.\n",
+ "\n",
+ "In contrast, the baseline inference fails to properly address the task and makes several incorrect assertions regarding relative sizes — for example, claiming that \"the brown cube is larger than the gray cube,\" which is not true.\n",
+ "\n",
+ "Notes:\n",
+ "\n",
+ " -In some cases, the reference model can actually provide better answers, particularly for very simple questions where a straightforward response suffices, whereas the GRPO-trained model might get lost in unnecessary reasoning.\n",
+ "\n",
+ " -Training could be further improved by incorporating more visually grounded rewards — for example, by requiring the model to output bounding boxes (BBOX) of the regions of interest — but this would require a fine-grained annotated dataset.\n",
+ "\n",
+ " -We could also enhance performance by training for more steps (currently only 200) and allowing longer input sequences, though this would significantly increase training time.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ZO2w8qY4Pw3X",
+ "metadata": {
+ "id": "ZO2w8qY4Pw3X"
+ },
+ "source": [
+ "### GGUF / llama.cpp Conversion\n",
+ "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
+ "\n",
+ "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
+ "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
+ "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
+ "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n",
+ "\n",
+ "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "glA7xkKAPzcv",
+ "metadata": {
+ "id": "glA7xkKAPzcv"
+ },
+ "outputs": [],
+ "source": [
+ "# Save to 8bit Q8_0\n",
+ "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
+ "# Remember to go to https://huggingface.co/settings/tokens for a token!\n",
+ "# And change hf to your username!\n",
+ "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
+ "\n",
+ "# Save to 16bit GGUF\n",
+ "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
+ "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
+ "\n",
+ "# Save to q4_k_m GGUF\n",
+ "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
+ "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n",
+ "\n",
+ "# Save to multiple GGUF options - much faster if you want multiple!\n",
+ "if False:\n",
+ " model.push_to_hub_gguf(\n",
+ " \"hf/model\", # Change hf to your username!\n",
+ " tokenizer,\n",
+ " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n",
+ " token = \"\",\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "kH9qNWAjO1-d",
+ "metadata": {
+ "id": "kH9qNWAjO1-d"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
+ "\n",
+ "Some other links:\n",
+ "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
+ "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
+ "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
+ "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
+ "\n",
+ "\n",
+ " \n",
+ " ![](\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\") \n",
+ " \n",
+ "\n",
+ " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n",
+ " |