- reading paper : CV-arXiv-Daily
- zhengzhugithub/AwesomeComputerVision
- handong1587.github.io
- wenbihan/reproducible-image-denoising-state-of-the-art
- YapengTian/Single-Image-Super-Resolution
- LoSealL/VideoSuperResolution
- junyanz/CatPapers
- TerenceCYJ/3D-Hand-Pose-Estimation-Papers
- wangzheallen/awesome-human-pose-estimation
- visual tracker benchmark results
- gjy3035/Awesome-Crowd-Counting
- https://github.com/ChanChiChoi/awesome-Face_Recognition
- zhangqianhui/AdversarialNetsPapers
- lzhbrian/image-to-image-papers
- Jyouhou/SceneTextPapers
- chongyangtao/Awesome-Scene-Text-Recognition
- wanghaisheng/awesome-ocr
- ChenChengKuan/awesome-text-generation
- amusi/awesome-object-detection
- hoya012/deep_learning_object_detection
- DetectionTeamUCAS-TF
- facebookresearch/maskrcnn-benchmark-pytorch
- roytseng-tw/Detectron.pytorch
- willard-yuan/awesome-cbir-papers
- filipradenovic/cnnimageretrieval-pytorch
- Cysu/open-reid
- layumi/Person_reID_baseline_PyTorch
- michuanhaohao/reid-strong-baseline
- GeorgeSeif/Semantic-Segmentation-Suite-TF
- ansleliu/LightNet-PyTorch
- meetshah1995/pytorch-semseg
- speedinghzl/pytorch-segmentation-toolbox
- mrgloom/awesome-semantic-segmentation
- Semantic Segmentation
- Semantic Segmentation论文整理
- iphysresearch/DataSciComp: Active Competitons to Join
- geekinglcq/CDCS :Chinese Data Competitions' Solutions
- Data-Competition-TopSolution
- Opencv
- Vlfeat
- Sklearn
- Machine Learning in Action：Read machine learning and analyze code implementation.
- ChristosChristofidis/awesome-deep-learning
- deepleraning.ai-course ：Neural Networks and Deep Learning，Improving Deep Neural Networks，Convolutional Neural Network.
- Xiaoxiang College course study notes, PPT and resources are very detailed.
- CS231n:Convolutional Neural Networks for Visual Recognition;
- CS224n:Natural Language Processing with Deep Learning
header only, dependency-free deep learning framework in C++11
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.
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MatConvNet