Update README.md

Author: fancifulcrow

README.md

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+# Image Colorization using U-Net and GAN
+
+## Overview
+This project implements an advanced image colorization system that transforms grayscale images into naturally colorized outputs using deep learning. By combining U-Net architecture with Generative Adversarial Networks (GANs), our system learns to predict plausible colors for black and white images.
+
+## Setup
+To install all required dependencies:
+```
+pip install -r requirements.txt
+```
+
+## Data
+The model trains on the Flowers102 dataset, which provides high-quality flower images with diverse colors and patterns. While the dataset includes class labels, we don't use them since our colorization process is independent of flower species.
+
+We chose to work in the CIELAB color space over RGB for three key advantages: it separates brightness (L\*) from color (a\* and b\*), reduces the prediction space from three channels to two, and provides perceptually uniform color representation. This makes the training process more efficient and produces better results.
+
+## Model Architecture
+
+### Generator (U-Net)
+The generator implements a U-Net architecture with skip connections between encoder and decoder paths. These connections preserve spatial details by combining low-level features with high-level abstractions. The network receives grayscale input (L channel) and outputs color predictions (\*a\*b channels).
+
+### Discriminator (PatchGAN)
+The discriminator uses a PatchGAN design that classifies N×N patches as real or fake, rather than evaluating the entire image at once. This patch-based approach improves the quality of local textures and color transitions. The discriminator compares the generated colorization against real color images during training.
+
+## Results
+
+<p align="center">
+<img src="images/results.png">
+</p>
+
 ## References
 
 I. J. Goodfellow et al., (2014). [*Generative Adversarial Networks*](https://arxiv.org/pdf/1406.2661)

images/results.png

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+kornia
+matplotlib
+numpy
+pyyaml
+torch
+torchvision
+tqdm