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.
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.
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Classical Machine Learning
- Implementations of traditional computer vision algorithms.
- Examples of feature extraction, image segmentation, and more.
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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).
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Transfer Learning
- Fine-tuning pre-trained models for specific tasks.
- Utilizing transfer learning for efficient training on limited datasets.
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Transformer Models
- Exploring the integration of transformer architectures in computer vision.
- Attention mechanisms for image understanding and processing.