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End-To-End Computer Vision

Welcome to the "End-To-End Computer Vision" repository! This comprehensive collection explores the cutting edge of computer vision, showcasing a spectrum from classical machine learning techniques to state-of-the-art transformer models.

Overview

Computer vision is a rapidly evolving field with applications ranging from image recognition to object detection and image generation. This repository aims to provide a curated set of resources, tutorials, and implementations covering various aspects of computer vision, making it a one-stop destination for both beginners and experienced practitioners.

Contents

  • Classical Machine Learning

    • Implementations of traditional computer vision algorithms.
    • Examples of feature extraction, image segmentation, and more.

  • Deep Learning

    • Convolutional Neural Networks (CNNs) for image classification.
    • Object detection using popular architectures like YOLO and Faster R-CNN.
    • Image generation with Generative Adversarial Networks (GANs).

  • Transfer Learning

    • Fine-tuning pre-trained models for specific tasks.
    • Utilizing transfer learning for efficient training on limited datasets.

  • Transformer Models

    • Exploring the integration of transformer architectures in computer vision.
    • Attention mechanisms for image understanding and processing.