🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…

deep learning for image processing including classification and object-detection etc.

Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Code release for ConvNeXt model

The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919

PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)

Summary of related papers on visual attention. Related code will be released based on Jittor gradually.

Official PyTorch implementation of SegFormer

Github of the FaceForensics dataset

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

[ECCVW 2022] The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

Code release for ConvNeXt V2 model

这是一个unet-pytorch的源码，可以训练自己的模型

Awesome List of Attention Modules and Plug&Play Modules in Computer Vision

A clean and concise Python implementation of SIFT (Scale-Invariant Feature Transform)

[CVPR 2020] CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement

code and trained models for "Attentional Feature Fusion"

This is the official repository for our recent work: PIDNet

[CVPR 2020] A Large-Scale Dataset for Real-World Face Forgery Detection

Richer Convolutional Features for Edge Detection model in pytorch CVPR2017

[CVPR 2023] Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation

这是各个主干网络分类模型的源码，可以用于训练自己的分类模型。

Wavelet Convolutions for Large Receptive Fields. ECCV 2024.

The code for the CVPR2019 paper Bi-Directional Cascade Network for Perceptual Edge Detection