Computer Vision Projects

Welcome to the Computer Vision Projects repository! This collection of projects demonstrates the power of computer vision techniques applied to real-world problems. From image classification to object detection, each project is designed to help you explore and learn the fundamentals of computer vision.

Table of Contents

• About
• Projects
• Installation
• Usage
• Technologies Used
• Contributing
• License
• Contact

About

This repository contains a diverse range of computer vision projects that utilize state-of-the-art models and libraries. Each project is structured with clean and well-documented code, making it easy to understand and replicate the results.

Whether you're a beginner looking to learn the basics or an experienced practitioner, these projects will help you deepen your knowledge of computer vision concepts such as image recognition, segmentation, and object detection.

Projects

1. Image Classification

• Description: A project that classifies images into predefined categories using convolutional neural networks (CNNs).
• Key Features:
  • Utilizes transfer learning with pre-trained models.
  • Achieves high accuracy on various image datasets.
• Tech Stack: Python, TensorFlow, Keras

2. Object Detection

• Description: Detect and classify objects in images and videos in real time.
• Key Features:
  • Implements YOLO and SSD models.
  • Real-time object tracking and bounding box creation.
• Tech Stack: Python, OpenCV, PyTorch

3. Image Segmentation

• Description: Segment different regions of an image using deep learning techniques.
• Key Features:
  • U-Net architecture for accurate pixel-wise segmentation.
  • Applications in medical imaging, autonomous driving, etc.
• Tech Stack: Python, PyTorch, OpenCV

(Add additional projects here as needed)

Installation

To get started with any of these projects, clone this repository to your local machine:

git clone https://github.com/Aryan-Chharia/Computer-Vision-Projects.git

Then, navigate to the project directory and install the required dependencies:

cd <project-folder>
pip install -r requirements.txt

Make sure you have Python 3.8+ installed on your system.

Usage

Each project contains detailed instructions in its respective folder. For general usage:

1. Navigate to the project folder.
2. Run the main script to start training/inference.
3. Follow the instructions in the README of each project.

Example for running an object detection model:

python object_detection.py --input <input_image_or_video>

Technologies Used

• Languages: Python
• Frameworks: TensorFlow, PyTorch, Keras
• Libraries: OpenCV, Scikit-learn, Matplotlib, NumPy

Contributing

We welcome contributions from the community! To contribute:

1. Fork the repository.
2. Create a new branch.

   git checkout -b feature-branch

3. Make your changes and commit

   git commit -m 'Add new feature

4. Push to the branch

   git push origin feature-branch

5. Open a pull request. Check the Contributing Guidelines for more details.

👥 Our Valuable Contributors ❤️✨

Thanks to all the amazing people who have contributed to Computer-Vision-Projects! 💖

License

This repository is licensed under the MIT License. See the LICENSE file for more details.