Object Detection Using Deep Learning

This project implements an object detection pipeline using deep learning techniques to identify and localize objects within images.

The goal of the project is to explore computer vision concepts such as image preprocessing, model inference, and bounding box prediction using a practical, end-to-end workflow.

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Overview

Object detection is a fundamental computer vision task that involves both classification (what the object is) and localization (where the object is in the image).
This project demonstrates how a trained deep learning model can be used to detect objects and visualize predictions with bounding boxes.

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Key Features

• Image-based object detection using a deep learning model
• Preprocessing of input images for inference
• Visualization of detected objects with bounding boxes
• Modular and readable implementation inside a Jupyter Notebook

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Implementation

The project is implemented in a Jupyter Notebook and follows a clear workflow:

1. Load and preprocess input images
2. Apply a pre-trained object detection model
3. Generate predictions and bounding boxes
4. Visualize detection results

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Tech Stack

• Python
• Jupyter Notebook
• Deep Learning / Computer Vision libraries (e.g., TensorFlow / PyTorch, OpenCV, NumPy)

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How to Run

1. Clone the repository
2. Open the notebook:

Screenshots