Stars
All Algorithms implemented in Python
Models and examples built with TensorFlow
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
OpenMMLab Detection Toolbox and Benchmark
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
End-to-End Object Detection with Transformers
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
A faster pytorch implementation of faster r-cnn
Implementation of Graph Convolutional Networks in TensorFlow
ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset
Deformable Convolutional Networks
A Tensorflow implementation of CapsNet(Capsules Net) in paper Dynamic Routing Between Capsules
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"
A PyTorch implementation of the YOLO v3 object detection algorithm
Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.
This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
SNIPER / AutoFocus is an efficient multi-scale object detection training / inference algorithm
Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
Dual Attention Network for Scene Segmentation (CVPR2019)
Pytorch implementation of RetinaNet object detection.
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)
[NeurIPS‘2021] "TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up", Yifan Jiang, Shiyu Chang, Zhangyang Wang
CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).