Stars
Documents of China Qualification of Computer and Software Professional Certificate (RuanKao). 计算机技术与软件专业技术资格(软考)文档资料
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
Code for <Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training> in ECCV18
『Java八股文』Java面试套路，Java进阶学习，打破内卷拿大厂Offer，升职加薪！
python 数据结构与算法 leetcode 算法题与书籍 刷算法全靠套路与总结！Crack LeetCode, not only how, but also why.
Stable Diffusion web UI
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥
深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）
Interview = 简历指南 + 算法题 + 八股文 + 源码分析
A faster pytorch implementation of faster r-cnn
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix
State-of-the-art 2D and 3D Face Analysis Project
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
This is the official implementation of "Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation".
Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).
ideas from paper 'BoxSup' and 'Simple does it' and realize it on sputum smear and drone images
Weakly Supervised Segmentation by Tensorflow. Implements semantic segmentation in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).
Implementation of NeurIPS 2019 paper "Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior"