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[NeurIPS 2021] LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
计算机基础（计算机网络/操作系统/数据库/Git...）面试问题全面总结，包含详细的follow-up question以及答案；全部采用【问题+追问+答案】的形式，即拿即用，直击互联网大厂面试；可用于模拟面试、面试前复习、短期内快速备战面试...
「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试，首选 JavaGuide！
In 2019, I prepare for the interview of Airbnb Beijing, the repo includes the coding questions I solved.
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
955 不加班的公司名单 - 工作 955，work–life balance (工作与生活的平衡)
Official Pytorch implementation of CutMix regularizer
mixup: Beyond Empirical Risk Minimization
Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning
A collection of loss functions for medical image segmentation
Edge-aware U-Net with CRF-RNN layer for Medical Image Segmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations (CVPR 2022 Oral)
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
command-line program for multispectral pan-sharpening (Python). Includes Brovey, FIHS (Fast Intensity Hue Saturation), Wavelet, and PCA (Principal Component Analysis).
PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.
Neighborhood-informed clustering for label super-resolution.
Code for training and testing deep learning based land cover models.
Learning to ground explanations of affect for visual art.
Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
Go RPC framework with high-performance and strong-extensibility for building micro-services.
《The Way to Go》中文译本，中文正式名《Go 入门指南》
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
This is an official implementation for "Self-Supervised Learning with Swin Transformers".