Computer vision papers from scratch

Repository for implementing computer vision papers from scratch. Each model is self contained(no imports from other packages/models, only torch and base python) and are easy to read/understand. Goal of this repository is to have a quick lookup place for the most famous models/training techniques for seamless integration in your current workflow

Papers implemented:

• Vision transformer needs register
• U-Net: Convolutional Networks for Biomedical Image Segmentation
• Resnet: Deep Residual Learning for Image Recognition
• DETR: End-to-End Object Detection with Transformers
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Papers to be implemented:

Model architectures

• DINO: Emerging Properties in Self-Supervised Vision Transformers
• DINOv2: Learning Robust Visual Features without Supervision
• Convnext - A ConvNet for the 2020s

Training techniques

• RandAugment: Practical automated data augmentation with a reduced search space

• CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

• Label smoothing: Rethinking the inception architecture for computer vision

• and others