├── LICENSE
├── README.md
└── scripts
└── generate_stats.py
/LICENSE:
--------------------------------------------------------------------------------
1 | The MIT License (MIT)
2 |
3 | Copyright (c) 2016 Aymeric Damien
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Top Deep Learning Projects
2 | A list of popular github projects related to deep learning (ranked by stars).
3 |
4 | Last Update: 2020.07.09
5 | | Project Name | Stars | Description |
6 | | ------- | ------ | ------ |
7 | |[tensorflow](https://github.com/tensorflow/tensorflow)|146k|An Open Source Machine Learning Framework for Everyone|
8 | |[keras](https://github.com/keras-team/keras)|48.9k|Deep Learning for humans|
9 | |[opencv](https://github.com/opencv/opencv)|46.1k|Open Source Computer Vision Library|
10 | |[pytorch](https://github.com/pytorch/pytorch)|40k|Tensors and Dynamic neural networks in Python with strong GPU acceleration|
11 | |[TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples)|38.1k|TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)|
12 | |[tesseract](https://github.com/tesseract-ocr/tesseract)|35.3k|Tesseract Open Source OCR Engine (main repository)|
13 | |[face_recognition](https://github.com/ageitgey/face_recognition)|35.2k|The world's simplest facial recognition api for Python and the command line|
14 | |[faceswap](https://github.com/deepfakes/faceswap)|31.4k|Deepfakes Software For All|
15 | |[transformers](https://github.com/huggingface/transformers)|30.4k|🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.|
16 | |[100-Days-Of-ML-Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code)|29.1k|100 Days of ML Coding|
17 | |[julia](https://github.com/JuliaLang/julia)|28.1k|The Julia Language: A fresh approach to technical computing.|
18 | |[gold-miner](https://github.com/xitu/gold-miner)|26.6k|🥇掘金翻译计划，可能是世界最大最好的英译中技术社区，最懂读者和译者的翻译平台：|
19 | |[awesome-scalability](https://github.com/binhnguyennus/awesome-scalability)|26.6k|The Patterns of Scalable, Reliable, and Performant Large-Scale Systems|
20 | |[basics](https://github.com/madewithml/basics)|24.5k|📚 Learn ML with clean code, simplified math and illustrative visuals.|
21 | |[bert](https://github.com/google-research/bert)|23.9k|TensorFlow code and pre-trained models for BERT|
22 | |[funNLP](https://github.com/fighting41love/funNLP)|22.1k|(Machine Learning)NLP面试中常考到的知识点和代码实现、nlp4han:中文自然语言处理工具集(断句/分词/词性标注/组块/句法分析/语义分析/NER/N元语法/HMM/代词消解/情感分析/拼写检查、XLM：Face…|
23 | |[xgboost](https://github.com/dmlc/xgboost)|19.4k|Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow|
24 | |[Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning)|18.4k|Clone a voice in 5 seconds to generate arbitrary speech in real-time|
25 | |[d2l-zh](https://github.com/d2l-ai/d2l-zh)|17.9k|《动手学深度学习》：面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。|
26 | |[openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)|17.8k|OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation|
27 | |[Coursera-ML-AndrewNg-Notes](https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes)|17.7k|吴恩达老师的机器学习课程个人笔记|
28 | |[DeepFaceLab](https://github.com/iperov/DeepFaceLab)|17.3k|DeepFaceLab is the leading software for creating deepfakes.|
29 | |[pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial)|17.3k|PyTorch Tutorial for Deep Learning Researchers|
30 | |[Mask_RCNN](https://github.com/matterport/Mask_RCNN)|17.2k|Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow|
31 | |[spaCy](https://github.com/explosion/spaCy)|16.8k|💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython|
32 | |[NLP-progress](https://github.com/sebastianruder/NLP-progress)|16.2k|Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.|
33 | |[100-Days-Of-ML-Code](https://github.com/MLEveryday/100-Days-Of-ML-Code)|15.6k|100-Days-Of-ML-Code中文版|
34 | |[cs-video-courses](https://github.com/Developer-Y/cs-video-courses)|14.9k|List of Computer Science courses with video lectures.|
35 | |[WaveFunctionCollapse](https://github.com/mxgmn/WaveFunctionCollapse)|14.7k|Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics.|
36 | |[lectures](https://github.com/oxford-cs-deepnlp-2017/lectures)|14.7k|Oxford Deep NLP 2017 course|
37 | |[reinforcement-learning](https://github.com/dennybritz/reinforcement-learning)|14.7k|Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accom…|
38 | |[pwc](https://github.com/zziz/pwc)|14.7k|Papers with code. Sorted by stars. Updated weekly.|
39 | |[TensorFlow-Course](https://github.com/machinelearningmindset/TensorFlow-Course)|14.6k|Simple and ready-to-use tutorials for TensorFlow|
40 | |[DeepSpeech](https://github.com/mozilla/DeepSpeech)|14.4k|A TensorFlow implementation of Baidu's DeepSpeech architecture|
41 | |[pumpkin-book](https://github.com/datawhalechina/pumpkin-book)|14k|《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book|
42 | |[tfjs](https://github.com/tensorflow/tfjs)|13.5k|A WebGL accelerated JavaScript library for training and deploying ML models.|
43 | |[examples](https://github.com/pytorch/examples)|13.5k|A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.|
44 | |[openface](https://github.com/cmusatyalab/openface)|13.5k|Face recognition with deep neural networks.|
45 | |[Qix](https://github.com/ty4z2008/Qix)|13.3k|Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang|
46 | |[spleeter](https://github.com/deezer/spleeter)|12.7k|Deezer source separation library including pretrained models.|
47 | |[Virgilio](https://github.com/virgili0/Virgilio)|12.7k|Your new Mentor for Data Science E-Learning.|
48 | |[nndl.github.io](https://github.com/nndl/nndl.github.io)|12.7k|《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning|
49 | |[Screenshot-to-code](https://github.com/emilwallner/Screenshot-to-code)|12.7k|A neural network that transforms a design mock-up into a static website.|
50 | |[pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)|12.4k|Image-to-Image Translation in PyTorch|
51 | |[pytorch-handbook](https://github.com/zergtant/pytorch-handbook)|11.9k|pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行|
52 | |[gun](https://github.com/amark/gun)|11.9k|An open source cybersecurity protocol for syncing decentralized graph data.|
53 | |[Paddle](https://github.com/PaddlePaddle/Paddle)|11.8k|PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训…|
54 | |[tensorflow-zh](https://github.com/jikexueyuanwiki/tensorflow-zh)|11.8k|谷歌全新开源人工智能系统TensorFlow官方文档中文版|
55 | |[darknet](https://github.com/AlexeyAB/darknet)|11.4k|YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )|
56 | |[learnopencv](https://github.com/spmallick/learnopencv)|11.4k|Learn OpenCV : C++ and Python Examples|
57 | |[neural-networks-and-deep-learning](https://github.com/mnielsen/neural-networks-and-deep-learning)|11.3k|Code samples for my book "Neural Networks and Deep Learning"|
58 | |[google-research](https://github.com/google-research/google-research)|11.2k|Google Research|
59 | |[labelImg](https://github.com/tzutalin/labelImg)|11.2k|🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images|
60 | |[gensim](https://github.com/RaRe-Technologies/gensim)|11k|Topic Modelling for Humans|
61 | |[pix2code](https://github.com/tonybeltramelli/pix2code)|10.9k|pix2code: Generating Code from a Graphical User Interface Screenshot|
62 | |[facenet](https://github.com/davidsandberg/facenet)|10.8k|Face recognition using Tensorflow|
63 | |[DeOldify](https://github.com/jantic/DeOldify)|10.7k|A Deep Learning based project for colorizing and restoring old images (and video!)|
64 | |[python-machine-learning-book](https://github.com/rasbt/python-machine-learning-book)|10.7k|The "Python Machine Learning (1st edition)" book code repository and info resource|
65 | |[stanford-cs-229-machine-learning](https://github.com/afshinea/stanford-cs-229-machine-learning)|10.6k|VIP cheatsheets for Stanford's CS 229 Machine Learning|
66 | |[mmdetection](https://github.com/open-mmlab/mmdetection)|10.5k|OpenMMLab Detection Toolbox and Benchmark|
67 | |[face-api.js](https://github.com/justadudewhohacks/face-api.js)|10.4k|JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js|
68 | |[Awesome-pytorch-list](https://github.com/bharathgs/Awesome-pytorch-list)|10.4k|A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,t…|
69 | |[nsfw_data_scraper](https://github.com/alex000kim/nsfw_data_scraper)|10.2k|Collection of scripts to aggregate image data for the purposes of training an NSFW Image Classifier|
70 | |[convnetjs](https://github.com/karpathy/convnetjs)|10k|Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.|
71 | |[CycleGAN](https://github.com/junyanz/CycleGAN)|9.8k|Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.|
72 | |[streamlit](https://github.com/streamlit/streamlit)|9.8k|Streamlit — The fastest way to build data apps in Python|
73 | |[DeepCreamPy](https://github.com/deeppomf/DeepCreamPy)|9.7k|Decensoring Hentai with Deep Neural Networks|
74 | |[stylegan](https://github.com/NVlabs/stylegan)|9.7k|StyleGAN - Official TensorFlow Implementation|
75 | |[Dive-into-DL-PyTorch](https://github.com/ShusenTang/Dive-into-DL-PyTorch)|9.6k|本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。|
76 | |[stanford-tensorflow-tutorials](https://github.com/chiphuyen/stanford-tensorflow-tutorials)|9.6k|This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.|
77 | |[horovod](https://github.com/horovod/horovod)|9.6k|Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.|
78 | |[Deep-Learning-with-TensorFlow-book](https://github.com/dragen1860/Deep-Learning-with-TensorFlow-book)|9.4k|深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.|
79 | |[neural-doodle](https://github.com/alexjc/neural-doodle)|9.4k|Turn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.)|
80 | |[caire](https://github.com/esimov/caire)|9.3k|Content aware image resize library|
81 | |[fast-style-transfer](https://github.com/lengstrom/fast-style-transfer)|9.2k|TensorFlow CNN for fast style transfer ⚡🖥🎨🖼|
82 | |[ncnn](https://github.com/Tencent/ncnn)|9.2k|ncnn is a high-performance neural network inference framework optimized for the mobile platform|
83 | |[kubeflow](https://github.com/kubeflow/kubeflow)|9.1k|Machine Learning Toolkit for Kubernetes|
84 | |[nltk](https://github.com/nltk/nltk)|9k|NLTK Source|
85 | |[flair](https://github.com/flairNLP/flair)|9k|A very simple framework for state-of-the-art Natural Language Processing (NLP)|
86 | |[ml-agents](https://github.com/Unity-Technologies/ml-agents)|9k|Unity Machine Learning Agents Toolkit|
87 | |[allennlp](https://github.com/allenai/allennlp)|8.8k|An open-source NLP research library, built on PyTorch.|
88 | |[botpress](https://github.com/botpress/botpress)|8.8k|🤖 The Conversational Platform with built-in language understanding (NLU), beautiful graphical interface and Dialog Manager (DM). Easily create chatbots and AI-based virtual assistants.|
89 | |[the-gan-zoo](https://github.com/hindupuravinash/the-gan-zoo)|8.7k|A list of all named GANs!|
90 | |[EffectiveTensorflow](https://github.com/vahidk/EffectiveTensorflow)|8.6k|TensorFlow tutorials and best practices.|
91 | |[tfjs-core](https://github.com/tensorflow/tfjs-core)|8.5k|WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.|
92 | |[fairseq](https://github.com/pytorch/fairseq)|8.4k|Facebook AI Research Sequence-to-Sequence Toolkit written in Python.|
93 | |[sonnet](https://github.com/deepmind/sonnet)|8.4k|TensorFlow-based neural network library|
94 | |[mit-deep-learning-book-pdf](https://github.com/janishar/mit-deep-learning-book-pdf)|8.3k|MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville|
95 | |[TensorFlow-Tutorials](https://github.com/Hvass-Labs/TensorFlow-Tutorials)|8.3k|TensorFlow Tutorials with YouTube Videos|
96 | |[pytorch_geometric](https://github.com/rusty1s/pytorch_geometric)|8.2k|Geometric Deep Learning Extension Library for PyTorch|
97 | |[tutorials](https://github.com/MorvanZhou/tutorials)|8.2k|机器学习相关教程|
98 | |[fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)|8k|A MNIST-like fashion product database. Benchmark 👉|
99 | |[bert-as-service](https://github.com/hanxiao/bert-as-service)|7.9k|Mapping a variable-length sentence to a fixed-length vector using BERT model|
100 | |[pix2pix](https://github.com/phillipi/pix2pix)|7.8k|Image-to-image translation with conditional adversarial nets|
101 | |[mediapipe](https://github.com/google/mediapipe)|7.7k|MediaPipe is the simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web.|
102 | |[recommenders](https://github.com/microsoft/recommenders)|7.7k|Best Practices on Recommendation Systems|
103 | |[mit-deep-learning](https://github.com/lexfridman/mit-deep-learning)|7.7k|Tutorials, assignments, and competitions for MIT Deep Learning related courses.|
104 | |[pytorch-book](https://github.com/chenyuntc/pytorch-book)|7.6k|PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)|
105 | |[Winds](https://github.com/GetStream/Winds)|7.6k|A Beautiful Open Source RSS & Podcast App Powered by Getstream.io|
106 | |[vid2vid](https://github.com/NVIDIA/vid2vid)|7.4k|Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.|
107 | |[Learn_Machine_Learning_in_3_Months](https://github.com/llSourcell/Learn_Machine_Learning_in_3_Months)|7.3k|This is the code for "Learn Machine Learning in 3 Months" by Siraj Raval on Youtube|
108 | |[golearn](https://github.com/sjwhitworth/golearn)|7.3k|Machine Learning for Go|
109 | |[Keras-GAN](https://github.com/eriklindernoren/Keras-GAN)|7.2k|Keras implementations of Generative Adversarial Networks.|
110 | |[mlcourse.ai](https://github.com/Yorko/mlcourse.ai)|7k|Open Machine Learning Course|
111 | |[faceai](https://github.com/vipstone/faceai)|7k|一款入门级的人脸、视频、文字检测以及识别的项目.|
112 | |[pysc2](https://github.com/deepmind/pysc2)|6.9k|StarCraft II Learning Environment|
113 | |[pretrained-models.pytorch](https://github.com/Cadene/pretrained-models.pytorch)|6.9k|Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.|
114 | |[PyTorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN)|6.7k|PyTorch implementations of Generative Adversarial Networks.|
115 | |[vision](https://github.com/pytorch/vision)|6.7k|Datasets, Transforms and Models specific to Computer Vision|
116 | |[nlp-tutorial](https://github.com/graykode/nlp-tutorial)|6.6k|Natural Language Processing Tutorial for Deep Learning Researchers|
117 | |[bullet3](https://github.com/bulletphysics/bullet3)|6.6k|Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics,|
118 | |[DCGAN-tensorflow](https://github.com/carpedm20/DCGAN-tensorflow)|6.6k|A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"|
119 | |[tfjs-models](https://github.com/tensorflow/tfjs-models)|6.5k|Pretrained models for TensorFlow.js|
120 | |[abu](https://github.com/bbfamily/abu)|6.5k|阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构|
121 | |[pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning)|6.5k|The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate|
122 | |[tensorboardX](https://github.com/lanpa/tensorboardX)|6.4k|tensorboard for pytorch (and chainer, mxnet, numpy, ...)|
123 | |[machine-learning-course](https://github.com/machinelearningmindset/machine-learning-course)|6.4k|💬 Machine Learning Course with Python:|
124 | |[guess](https://github.com/guess-js/guess)|6.3k|🔮 Libraries & tools for enabling Machine Learning driven user-experiences on the web|
125 | |[pyro](https://github.com/pyro-ppl/pyro)|6.3k|Deep universal probabilistic programming with Python and PyTorch|
126 | |[lab](https://github.com/deepmind/lab)|6.2k|A customisable 3D platform for agent-based AI research|
127 | |[mml-book.github.io](https://github.com/mml-book/mml-book.github.io)|6.2k|Companion webpage to the book "Mathematics For Machine Learning"|
128 | |[Interview](https://github.com/apachecn/Interview)|6.2k|Interview = 简历指南 + LeetCode + Kaggle|
129 | |[tensorlayer](https://github.com/tensorlayer/tensorlayer)|6.2k|Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥|
130 | |[generative-models](https://github.com/wiseodd/generative-models)|6.1k|Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.|
131 | |[machine-learning-yearning-cn](https://github.com/deeplearning-ai/machine-learning-yearning-cn)|6.1k|Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著|
132 | |[keras-yolo3](https://github.com/qqwweee/keras-yolo3)|6k|A Keras implementation of YOLOv3 (Tensorflow backend)|
133 | |[BossSensor](https://github.com/Hironsan/BossSensor)|5.9k|Hide screen when boss is approaching.|
134 | |[tensorflow2_tutorials_chinese](https://github.com/czy36mengfei/tensorflow2_tutorials_chinese)|5.9k|tensorflow2中文教程，持续更新(当前版本:tensorflow2.0)，tag: tensorflow 2.0 tutorials|
135 | |[TensorFlow-Tutorials](https://github.com/nlintz/TensorFlow-Tutorials)|5.9k|Simple tutorials using Google's TensorFlow Framework|
136 | |[argo](https://github.com/argoproj/argo)|5.9k|Argo Workflows: Get stuff done with Kubernetes.|
137 | |[python-machine-learning-book-2nd-edition](https://github.com/rasbt/python-machine-learning-book-2nd-edition)|5.8k|The "Python Machine Learning (2nd edition)" book code repository and info resource|
138 | |[dvc](https://github.com/iterative/dvc)|5.7k|🦉Data Version Control | Git for Data & Models|
139 | |[EasyPR](https://github.com/liuruoze/EasyPR)|5.7k|An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations.|
140 | |[AdversarialNetsPapers](https://github.com/zhangqianhui/AdversarialNetsPapers)|5.6k|The classical paper list with code about generative adversarial nets|
141 | |[tensorpack](https://github.com/tensorpack/tensorpack)|5.6k|A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility|
142 | |[photoprism](https://github.com/photoprism/photoprism)|5.6k|Personal Photo Management powered by Go and Google TensorFlow|
143 | |[tensorflow_cookbook](https://github.com/nfmcclure/tensorflow_cookbook)|5.6k|Code for Tensorflow Machine Learning Cookbook|
144 | |[albumentations](https://github.com/albumentations-team/albumentations)|5.6k|fast image augmentation library and easy to use wrapper around other libraries|
145 | |[swift](https://github.com/tensorflow/swift)|5.6k|Swift for TensorFlow|
146 | |[darkflow](https://github.com/thtrieu/darkflow)|5.6k|Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices|
147 | |[tensorflow_tutorials](https://github.com/pkmital/tensorflow_tutorials)|5.5k|From the basics to slightly more interesting applications of Tensorflow|
148 | |[deep-learning-coursera](https://github.com/Kulbear/deep-learning-coursera)|5.5k|Deep Learning Specialization by Andrew Ng on Coursera.|
149 | |[transferlearning](https://github.com/jindongwang/transferlearning)|5.5k|Everything about Transfer Learning and Domain Adaptation--迁移学习|
150 | |[ML-NLP](https://github.com/NLP-LOVE/ML-NLP)|5.5k|此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。|
151 | |[nmt](https://github.com/tensorflow/nmt)|5.5k|TensorFlow Neural Machine Translation Tutorial|
152 | |[faster-rcnn.pytorch](https://github.com/jwyang/faster-rcnn.pytorch)|5.5k|A faster pytorch implementation of faster r-cnn|
153 | |[UGATIT](https://github.com/taki0112/UGATIT)|5.4k|Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Inst…|
154 | |[pandas-profiling](https://github.com/pandas-profiling/pandas-profiling)|5.4k|Create HTML profiling reports from pandas DataFrame objects|
155 | |[deep-residual-networks](https://github.com/KaimingHe/deep-residual-networks)|5.4k|Deep Residual Learning for Image Recognition|
156 | |[xlnet](https://github.com/zihangdai/xlnet)|5.3k|XLNet: Generalized Autoregressive Pretraining for Language Understanding|
157 | |[leeml-notes](https://github.com/datawhalechina/leeml-notes)|5.2k|李宏毅《机器学习》笔记，在线阅读地址：https://datawhalechina.github.io/leeml-notes|
158 | |[wav2letter](https://github.com/facebookresearch/wav2letter)|5.2k|Facebook AI Research's Automatic Speech Recognition Toolkit|
159 | |[neural-style](https://github.com/anishathalye/neural-style)|5.2k|Neural style in TensorFlow! 🎨|
160 | |[CVPR2020-Paper-Code-Interpretation](https://github.com/extreme-assistant/CVPR2020-Paper-Code-Interpretation)|5.2k|cvpr2020/cvpr2019／cvpr2018/cvpr2017 papers，极市团队整理|
161 | |[TensorFlow-2.x-Tutorials](https://github.com/dragen1860/TensorFlow-2.x-Tutorials)|5.2k|TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT exampl…|
162 | |[yolov3](https://github.com/ultralytics/yolov3)|5.2k|YOLOv3 in PyTorch > ONNX > CoreML > iOS|
163 | |[cnn-text-classification-tf](https://github.com/dennybritz/cnn-text-classification-tf)|5.2k|Convolutional Neural Network for Text Classification in Tensorflow|
164 | |[seq2seq](https://github.com/google/seq2seq)|5.2k|A general-purpose encoder-decoder framework for Tensorflow|
165 | |[chineseocr_lite](https://github.com/ouyanghuiyu/chineseocr_lite)|5.1k|超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 , dbnet(1.7M) + crnn(6.3M) + anglenet(1.5M) 总模型仅10M|
166 | |[featuretools](https://github.com/FeatureLabs/featuretools)|5k|An open source python library for automated feature engineering|
167 | |[labelme](https://github.com/wkentaro/labelme)|5k|Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).|
168 | |[ImageAI](https://github.com/OlafenwaMoses/ImageAI)|5k|A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities|
169 | |[nlp-recipes](https://github.com/microsoft/nlp-recipes)|5k|Natural Language Processing Best Practices & Examples|
170 | |[have-fun-with-machine-learning](https://github.com/humphd/have-fun-with-machine-learning)|4.9k|An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks|
171 | |[eat_tensorflow2_in_30_days](https://github.com/lyhue1991/eat_tensorflow2_in_30_days)|4.9k|Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋|
172 | |[tensorflow-wavenet](https://github.com/ibab/tensorflow-wavenet)|4.9k|A TensorFlow implementation of DeepMind's WaveNet paper|
173 | |[PyTorch-Tutorial](https://github.com/MorvanZhou/PyTorch-Tutorial)|4.9k|Build your neural network easy and fast|
174 | |[stylegan2](https://github.com/NVlabs/stylegan2)|4.9k|StyleGAN2 - Official TensorFlow Implementation|
175 | |[h2o-3](https://github.com/h2oai/h2o-3)|4.9k|Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBo…|
176 | |[awesome-machine-learning-on-source-code](https://github.com/src-d/awesome-machine-learning-on-source-code)|4.8k|Cool links & research papers related to Machine Learning applied to source code (MLonCode)|
177 | |[Learning-to-See-in-the-Dark](https://github.com/cchen156/Learning-to-See-in-the-Dark)|4.8k|Learning to See in the Dark. CVPR 2018|
178 | |[PyTorch-YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3)|4.8k|Minimal PyTorch implementation of YOLOv3|
179 | |[first-order-model](https://github.com/AliaksandrSiarohin/first-order-model)|4.8k|This repository contains the source code for the paper First Order Motion Model for Image Animation|
180 | |[models](https://github.com/PaddlePaddle/models)|4.8k|Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证的深度学习模型）|
181 | |[smile](https://github.com/haifengl/smile)|4.8k|Statistical Machine Intelligence & Learning Engine|
182 | |[keras-js](https://github.com/transcranial/keras-js)|4.7k|Run Keras models in the browser, with GPU support using WebGL|
183 | |[carla](https://github.com/carla-simulator/carla)|4.7k|Open-source simulator for autonomous driving research.|
184 | |[keras-rl](https://github.com/keras-rl/keras-rl)|4.7k|Deep Reinforcement Learning for Keras.|
185 | |[useful-java-links](https://github.com/Vedenin/useful-java-links)|4.7k|A list of useful Java frameworks, libraries, software and hello worlds examples|
186 | |[Awesome-CoreML-Models](https://github.com/likedan/Awesome-CoreML-Models)|4.7k|Largest list of models for Core ML (for iOS 11+)|
187 | |[python-small-examples](https://github.com/jackzhenguo/python-small-examples)|4.7k|告别枯燥，致力于打造 Python 富有体系且实用的小例子、小案例。|
188 | |[gcn](https://github.com/tkipf/gcn)|4.7k|Implementation of Graph Convolutional Networks in TensorFlow|
189 | |[introduction_to_ml_with_python](https://github.com/amueller/introduction_to_ml_with_python)|4.7k|Notebooks and code for the book "Introduction to Machine Learning with Python"|
190 | |[stargan](https://github.com/yunjey/stargan)|4.6k|StarGAN - Official PyTorch Implementation (CVPR 2018)|
191 | |[pix2pixHD](https://github.com/NVIDIA/pix2pixHD)|4.6k|Synthesizing and manipulating 2048x1024 images with conditional GANs|
192 | |[Data-Science-Wiki](https://github.com/Leo-G/Data-Science-Wiki)|4.6k|A wiki of DataScience, Statistics, Maths, R,Python, AI, Machine Learning, Automation, Devops Tools, Bash, Linux Tutor…|
193 | |[MVision](https://github.com/Ewenwan/MVision)|4.6k|机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶|
194 | |[cleverhans](https://github.com/tensorflow/cleverhans)|4.6k|An adversarial example library for constructing attacks, building defenses, and benchmarking both|
195 | |[vaex](https://github.com/vaexio/vaex)|4.6k|Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀|
196 | |[Grokking-Deep-Learning](https://github.com/iamtrask/Grokking-Deep-Learning)|4.6k|this repository accompanies the book "Grokking Deep Learning"|
197 | |[trax](https://github.com/google/trax)|4.5k|Trax — Deep Learning with Clear Code and Speed|
198 | |[graph_nets](https://github.com/deepmind/graph_nets)|4.5k|Build Graph Nets in Tensorflow|
199 | |[edward](https://github.com/blei-lab/edward)|4.5k|A probabilistic programming language in TensorFlow. Deep generative models, variational inference.|
200 | |[TensorFlow-World](https://github.com/astorfi/TensorFlow-World)|4.5k|🌎 Simple and ready-to-use tutorials for TensorFlow|
201 | |[imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn)|4.5k|A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning|
202 | |[machine-learning-mindmap](https://github.com/dformoso/machine-learning-mindmap)|4.5k|A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.|
203 | |[seq2seq-couplet](https://github.com/wb14123/seq2seq-couplet)|4.5k|Play couplet with seq2seq model. 用深度学习对对联。|
204 | |[EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch)|4.4k|A PyTorch implementation of EfficientNet|
205 | |[TensorFlow-Book](https://github.com/BinRoot/TensorFlow-Book)|4.4k|Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.|
206 | |[stanza](https://github.com/stanfordnlp/stanza)|4.4k|Official Stanford NLP Python Library for Many Human Languages|
207 | |[amazon-dsstne](https://github.com/amzn/amazon-dsstne)|4.4k|Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) ma…|
208 | |[cnn-explainer](https://github.com/poloclub/cnn-explainer)|4.4k|Learning Convolutional Neural Networks with Interactive Visualization.|
209 | |[Realtime_Multi-Person_Pose_Estimation](https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation)|4.4k|Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)|
210 | |[stanford-cs-230-deep-learning](https://github.com/afshinea/stanford-cs-230-deep-learning)|4.3k|VIP cheatsheets for Stanford's CS 230 Deep Learning|
211 | |[Real-Time-Person-Removal](https://github.com/jasonmayes/Real-Time-Person-Removal)|4.3k|Removing people from complex backgrounds in real time using TensorFlow.js in the web browser|
212 | |[OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py)|4.3k|Open Source Neural Machine Translation in PyTorch|
213 | |[tensorflow_practice](https://github.com/princewen/tensorflow_practice)|4.3k|tensorflow实战练习，包括强化学习、推荐系统、nlp等|
214 | |[pytorch-cnn-visualizations](https://github.com/utkuozbulak/pytorch-cnn-visualizations)|4.2k|Pytorch implementation of convolutional neural network visualization techniques|
215 | |[tensorspace](https://github.com/tensorspace-team/tensorspace)|4.2k|Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep …|
216 | |[DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples)|4.2k|Deep Learning Examples|
217 | |[sketch-code](https://github.com/ashnkumar/sketch-code)|4.2k|Keras model to generate HTML code from hand-drawn website mockups. Implements an image captioning architecture to dra…|
218 | |[deeplearning-papernotes](https://github.com/dennybritz/deeplearning-papernotes)|4.2k|Summaries and notes on Deep Learning research papers|
219 | |[apex](https://github.com/NVIDIA/apex)|4.2k|A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch|
220 | |[AlphaPose](https://github.com/MVIG-SJTU/AlphaPose)|4.1k|Real-Time and Accurate Multi-Person Pose Estimation&Tracking System|
221 | |[attention-is-all-you-need-pytorch](https://github.com/jadore801120/attention-is-all-you-need-pytorch)|4.1k|A PyTorch implementation of the Transformer model in "Attention is All You Need".|
222 | |[nmap](https://github.com/nmap/nmap)|4.1k|Nmap - the Network Mapper. Github mirror of official SVN repository.|
223 | |[Machine-learning-learning-notes](https://github.com/Vay-keen/Machine-learning-learning-notes)|4.1k|周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有…|
224 | |[serenata-de-amor](https://github.com/okfn-brasil/serenata-de-amor)|4.1k|🕵 Artificial Intelligence for social control of public administration|
225 | |[practical-pytorch](https://github.com/spro/practical-pytorch)|4.1k|DEPRECATED and not maintained - see official repo at https://github.com/pytorch/tutorials|
226 | |[pytorch-image-models](https://github.com/rwightman/pytorch-image-models)|4.1k|PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MixNet, MobileNet-V3/V2, MNASNet, Single-Path NAS, FBNet, and more|
227 | |[face-alignment](https://github.com/1adrianb/face-alignment)|4k|🔥 2D and 3D Face alignment library build using pytorch|
228 | |[learning-to-learn](https://github.com/deepmind/learning-to-learn)|4k|Learning to Learn in TensorFlow|
229 | |[machine-learning-notes](https://github.com/roboticcam/machine-learning-notes)|4k|My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1500+ slides) 我不间断更…|
230 | |[umap](https://github.com/lmcinnes/umap)|4k|Uniform Manifold Approximation and Projection|
231 | |[DeepLearningZeroToAll](https://github.com/hunkim/DeepLearningZeroToAll)|4k|TensorFlow Basic Tutorial Labs|
232 | |[gluon-cv](https://github.com/dmlc/gluon-cv)|4k|Gluon CV Toolkit|
233 | |[pipeline](https://github.com/PipelineAI/pipeline)|4k|PipelineAI Kubeflow Distribution|
234 | |[snorkel](https://github.com/snorkel-team/snorkel)|4k|A system for quickly generating training data with weak supervision|
235 | |[DIGITS](https://github.com/NVIDIA/DIGITS)|4k|Deep Learning GPU Training System|
236 | |[DenseNet](https://github.com/liuzhuang13/DenseNet)|4k|Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).|
237 | |[awesome-project-ideas](https://github.com/NirantK/awesome-project-ideas)|4k|Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas|
238 | |[tutorials](https://github.com/pytorch/tutorials)|4k|PyTorch tutorials.|
239 | |[Deep-Learning-21-Examples](https://github.com/hzy46/Deep-Learning-21-Examples)|3.9k|《21个项目玩转深度学习———基于TensorFlow的实践详解》配套代码|
240 | |[DeepLearningTutorials](https://github.com/lisa-lab/DeepLearningTutorials)|3.9k|Deep Learning Tutorial notes and code. See the wiki for more info.|
241 | |[textgenrnn](https://github.com/minimaxir/textgenrnn)|3.9k|Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines …|
242 | |[lucid](https://github.com/tensorflow/lucid)|3.8k|A collection of infrastructure and tools for research in neural network interpretability.|
243 | |[nsfwjs](https://github.com/infinitered/nsfwjs)|3.8k|NSFW detection on the client-side via TensorFlow.js|
244 | |[ssd.pytorch](https://github.com/amdegroot/ssd.pytorch)|3.8k|A PyTorch Implementation of Single Shot MultiBox Detector|
245 | |[MachineLearning](https://github.com/wepe/MachineLearning)|3.8k|Basic Machine Learning and Deep Learning|
246 | |[Tensorflow-Tutorial](https://github.com/MorvanZhou/Tensorflow-Tutorial)|3.8k|Tensorflow tutorial from basic to hard|
247 | |[awesome-ml-for-cybersecurity](https://github.com/jivoi/awesome-ml-for-cybersecurity)|3.8k|Machine Learning for Cyber Security|
248 | |[daily-paper-computer-vision](https://github.com/amusi/daily-paper-computer-vision)|3.8k|记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文|
249 | |[SSD-Tensorflow](https://github.com/balancap/SSD-Tensorflow)|3.8k|Single Shot MultiBox Detector in TensorFlow|
250 | |[cvat](https://github.com/opencv/cvat)|3.8k|Powerful and efficient Computer Vision Annotation Tool (CVAT)|
251 | |[deep-learning-roadmap](https://github.com/machinelearningmindset/deep-learning-roadmap)|3.8k|📡 All You Need to Know About Deep Learning - A kick-starter|
252 | |[sqlflow](https://github.com/sql-machine-learning/sqlflow)|3.8k|Brings SQL and AI together.|
253 | |[mmf](https://github.com/facebookresearch/mmf)|3.7k|A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)|
254 | |[tensorflow-docs](https://github.com/xitu/tensorflow-docs)|3.7k|TensorFlow 最新官方文档中文版|
255 | |[iGAN](https://github.com/junyanz/iGAN)|3.7k|Interactive Image Generation via Generative Adversarial Networks|
256 | |[CapsNet-Tensorflow](https://github.com/naturomics/CapsNet-Tensorflow)|3.7k|A Tensorflow implementation of CapsNet(Capsules Net) in paper Dynamic Routing Between Capsules|
257 | |[Yet-Another-EfficientDet-Pytorch](https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch)|3.6k|The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.|
258 | |[pytorch-examples](https://github.com/jcjohnson/pytorch-examples)|3.6k|Simple examples to introduce PyTorch|
259 | |[ML_for_Hackers](https://github.com/johnmyleswhite/ML_for_Hackers)|3.6k|Code accompanying the book "Machine Learning for Hackers"|
260 | |[docs](https://github.com/tensorflow/docs)|3.6k|TensorFlow documentation|
261 | |[tensorflow-generative-model-collections](https://github.com/hwalsuklee/tensorflow-generative-model-collections)|3.6k|Collection of generative models in Tensorflow|
262 | |[DeepLearning.ai-Summary](https://github.com/mbadry1/DeepLearning.ai-Summary)|3.6k|This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed ever…|
263 | |[BERT-pytorch](https://github.com/codertimo/BERT-pytorch)|3.6k|Google AI 2018 BERT pytorch implementation|
264 | |[pwnagotchi](https://github.com/evilsocket/pwnagotchi)|3.5k|(⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.|
265 | |[HyperLPR](https://github.com/zeusees/HyperLPR)|3.5k|基于深度学习高性能中文车牌识别 High Performance Chinese License Plate Recognition Framework.|
266 | |[deep-learning](https://github.com/udacity/deep-learning)|3.5k|Repo for the Deep Learning Nanodegree Foundations program.|
267 | |[TensorFlowOnSpark](https://github.com/yahoo/TensorFlowOnSpark)|3.5k|TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.|
268 | |[BigDL](https://github.com/intel-analytics/BigDL)|3.5k|BigDL: Distributed Deep Learning Framework for Apache Spark|
269 | |[AlgoWiki](https://github.com/vicky002/AlgoWiki)|3.5k|Repository which contains links and resources on different topics of Computer Science.|
270 | |[examples](https://github.com/tensorflow/examples)|3.5k|TensorFlow examples|
271 | |[tf-faster-rcnn](https://github.com/endernewton/tf-faster-rcnn)|3.4k|Tensorflow Faster RCNN for Object Detection|
272 | |[tf-pose-estimation](https://github.com/ildoonet/tf-pose-estimation)|3.4k|Deep Pose Estimation implemented using Tensorflow with Custom Architectures for fast inference.|
273 | |[awesome-machine-learning-cn](https://github.com/jobbole/awesome-machine-learning-cn)|3.4k|机器学习资源大全中文版，包括机器学习领域的框架、库以及软件|
274 | |[metaflow](https://github.com/Netflix/metaflow)|3.4k|Build and manage real-life data science projects with ease.|
275 | |[deep-reinforcement-learning](https://github.com/udacity/deep-reinforcement-learning)|3.3k|Repo for the Deep Reinforcement Learning Nanodegree program|
276 | |[semantic-segmentation-pytorch](https://github.com/CSAILVision/semantic-segmentation-pytorch)|3.3k|Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset|
277 | |[gocv](https://github.com/hybridgroup/gocv)|3.3k|Go package for computer vision using OpenCV 4 and beyond.|
278 | |[d2l-pytorch](https://github.com/dsgiitr/d2l-pytorch)|3.3k|This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.|
279 | |[Chinese-BERT-wwm](https://github.com/ymcui/Chinese-BERT-wwm)|3.3k|Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）|
280 | |[SmartCropper](https://github.com/pqpo/SmartCropper)|3.3k|🔥 A library for cropping image in a smart way that can identify the border and correct the cropped image. 智能图片裁剪框架。自动识别边框，手动调节选区，使用透视变换裁剪并矫正选区；适用于身份证，名片，文档等照片的裁剪。|
281 | |[PyTorchZeroToAll](https://github.com/hunkim/PyTorchZeroToAll)|3.3k|Simple PyTorch Tutorials Zero to ALL!|
282 | |[pyAudioAnalysis](https://github.com/tyiannak/pyAudioAnalysis)|3.3k|Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications|
283 | |[InterpretableMLBook](https://github.com/MingchaoZhu/InterpretableMLBook)|3.3k|《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版|
284 | |[snips-nlu](https://github.com/snipsco/snips-nlu)|3.3k|Snips Python library to extract meaning from text|
285 | |[pyod](https://github.com/yzhao062/pyod)|3.3k|A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)|
286 | |[DeepLearning](https://github.com/Mikoto10032/DeepLearning)|3.2k|深度学习入门教程, 优秀文章, Deep Learning Tutorial|
287 | |[vespa](https://github.com/vespa-engine/vespa)|3.2k|Vespa is an engine for low-latency computation over large data sets.|
288 | |[deep-voice-conversion](https://github.com/andabi/deep-voice-conversion)|3.2k|Deep neural networks for voice conversion (voice style transfer) in Tensorflow|
289 | |[lightfm](https://github.com/lyst/lightfm)|3.2k|A Python implementation of LightFM, a hybrid recommendation algorithm.|
290 | |[machine-learning](https://github.com/udacity/machine-learning)|3.2k|Content for Udacity's Machine Learning curriculum|
291 | |[skflow](https://github.com/tensorflow/skflow)|3.2k|Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning|
292 | |[Tensorflow-Project-Template](https://github.com/MrGemy95/Tensorflow-Project-Template)|3.2k|A best practice for tensorflow project template architecture.|
293 | |[EasyOCR](https://github.com/JaidedAI/EasyOCR)|3.2k|Ready-to-use OCR with 40+ languages supported including Chinese, Japanese, Korean and Thai|
294 | |[text-classification-cnn-rnn](https://github.com/gaussic/text-classification-cnn-rnn)|3.1k|CNN-RNN中文文本分类，基于TensorFlow|
295 | |[MachineLearning_Python](https://github.com/lawlite19/MachineLearning_Python)|3.1k|机器学习算法python实现|
296 | |[imagededup](https://github.com/idealo/imagededup)|3.1k|😎 Finding duplicate images made easy!|
297 | |[MatchZoo](https://github.com/NTMC-Community/MatchZoo)|3.1k|Facilitating the design, comparison and sharing of deep text matching models.|
298 | |[transformer](https://github.com/Kyubyong/transformer)|3.1k|A TensorFlow Implementation of the Transformer: Attention Is All You Need|
299 | |[tensorflow_poems](https://github.com/jinfagang/tensorflow_poems)|3.1k|中文古诗自动作诗机器人，屌炸天，基于tensorflow1.10 api，正在积极维护升级中，快star，保持更新！|
300 | |[Deep-Learning-Roadmap](https://github.com/astorfi/Deep-Learning-Roadmap)|3.1k|📡 Organized Resources for Deep Learning Researchers and Developers|
301 | |[label-studio](https://github.com/heartexlabs/label-studio)|3.1k|Label Studio is a multi-type data labeling and annotation tool with standardized output format|
302 | |[ASRT_SpeechRecognition](https://github.com/nl8590687/ASRT_SpeechRecognition)|3.1k|A Deep-Learning-Based Chinese Speech Recognition System 基于深度学习的中文语音识别系统|
303 | |[benchmark_results](https://github.com/foolwood/benchmark_results)|3.1k|Visual Tracking Paper List|
304 | |[Machine-Learning](https://github.com/Jack-Cherish/Machine-Learning)|3k|⚡机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归|
305 | |[yolact](https://github.com/dbolya/yolact)|3k|A simple, fully convolutional model for real-time instance segmentation.|
306 | |[trfl](https://github.com/deepmind/trfl)|3k|TensorFlow Reinforcement Learning|
307 | |[pytorch-cifar](https://github.com/kuangliu/pytorch-cifar)|3k|95.16% on CIFAR10 with PyTorch|
308 | |[sacred](https://github.com/IDSIA/sacred)|3k|Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.|
309 | |[yolov5](https://github.com/ultralytics/yolov5)|3k|YOLOv5 in PyTorch > ONNX > CoreML > iOS|
310 | |[Reinforcement-Learning](https://github.com/andri27-ts/Reinforcement-Learning)|3k|Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning|
311 | |[distiller](https://github.com/NervanaSystems/distiller)|3k|Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://nervanasy…|
312 | |[FastMaskRCNN](https://github.com/CharlesShang/FastMaskRCNN)|3k|Mask RCNN in TensorFlow|
313 | |[probability](https://github.com/tensorflow/probability)|3k|Probabilistic reasoning and statistical analysis in TensorFlow|
314 | |[DeepVideoAnalytics](https://github.com/AKSHAYUBHAT/DeepVideoAnalytics)|2.9k|A distributed visual search and visual data analytics platform.|
315 | |[tensorwatch](https://github.com/microsoft/tensorwatch)|2.9k|Debugging, monitoring and visualization for Python Machine Learning and Data Science|
316 | |[darts](https://github.com/quark0/darts)|2.9k|Differentiable architecture search for convolutional and recurrent networks|
317 | |[computervision-recipes](https://github.com/microsoft/computervision-recipes)|2.9k|Best Practices, code samples, and documentation for Computer Vision.|
318 | |[text-detection-ctpn](https://github.com/eragonruan/text-detection-ctpn)|2.9k|text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network|
319 | |[tensorflow-windows-wheel](https://github.com/fo40225/tensorflow-windows-wheel)|2.9k|Tensorflow prebuilt binary for Windows|
320 | |[tensorflow-yolov3](https://github.com/YunYang1994/tensorflow-yolov3)|2.9k|🔥 Pure tensorflow Implement of YOLOv3 with support to train your own dataset|
321 | |[zhihu](https://github.com/NELSONZHAO/zhihu)|2.9k|This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.|
322 | |[BERT-BiLSTM-CRF-NER](https://github.com/macanv/BERT-BiLSTM-CRF-NER)|2.9k|Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services|
323 | |[TensorFlowSharp](https://github.com/migueldeicaza/TensorFlowSharp)|2.9k|TensorFlow API for .NET languages|
324 | |[ignite](https://github.com/pytorch/ignite)|2.9k|High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.|
325 | |[tensorflow-tutorial](https://github.com/caicloud/tensorflow-tutorial)|2.9k|Example TensorFlow codes and Caicloud TensorFlow as a Service dev environment.|
326 | |[100-Days-of-ML-Code-Chinese-Version](https://github.com/Avik-Jain/100-Days-of-ML-Code-Chinese-Version)|2.9k|Chinese Translation for Machine Learning Infographics|
327 | |[deep-learning-papers-translation](https://github.com/SnailTyan/deep-learning-papers-translation)|2.9k|深度学习论文翻译，包括分类论文，检测论文等|
328 | |[DMTK](https://github.com/microsoft/DMTK)|2.8k|Microsoft Distributed Machine Learning Toolkit|
329 | |[caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow)|2.8k|Caffe models in TensorFlow|
330 | |[libpostal](https://github.com/openvenues/libpostal)|2.8k|A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.|
331 | |[pigo](https://github.com/esimov/pigo)|2.8k|Pure Go face detection, pupil/eyes localization and facial landmark points detection library|
332 | |[mindsdb](https://github.com/mindsdb/mindsdb)|2.8k|Machine Learning in one line of code|
333 | |[Tensorflow-Cookbook](https://github.com/taki0112/Tensorflow-Cookbook)|2.8k|Simple Tensorflow Cookbook for easy-to-use|
334 | |[easy-tensorflow](https://github.com/easy-tensorflow/easy-tensorflow)|2.8k|Simple and comprehensive tutorials in TensorFlow|
335 | |[DeepLearning](https://github.com/yusugomori/DeepLearning)|2.8k|Deep Learning (Python, C, C++, Java, Scala, Go)|
336 | |[pytorch-yolo-v3](https://github.com/ayooshkathuria/pytorch-yolo-v3)|2.8k|A PyTorch implementation of the YOLO v3 object detection algorithm|
337 | |[jukebox](https://github.com/openai/jukebox)|2.8k|Code for the paper "Jukebox: A Generative Model for Music"|
338 | |[makegirlsmoe_web](https://github.com/makegirlsmoe/makegirlsmoe_web)|2.8k|Create Anime Characters with MakeGirlsMoe|
339 | |[deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow)|2.8k|Introduction to Deep Neural Networks with Keras and Tensorflow|
340 | |[SiamMask](https://github.com/foolwood/SiamMask)|2.7k|[CVPR2019] Fast Online Object Tracking and Segmentation: A Unifying Approach|
341 | |[tencent-ml-images](https://github.com/Tencent/tencent-ml-images)|2.7k|Largest multi-label image database; ResNet-101 model; 80.73% top-1 acc on ImageNet|
342 | |[DALI](https://github.com/NVIDIA/DALI)|2.7k|A library containing both highly optimized building blocks and an execution engine for data pre-processing in deep le…|
343 | |[shogun](https://github.com/shogun-toolbox/shogun)|2.7k|Shōgun|
344 | |[optuna](https://github.com/optuna/optuna)|2.7k|A hyperparameter optimization framework|
345 | |[Automatic_Speech_Recognition](https://github.com/zzw922cn/Automatic_Speech_Recognition)|2.7k|End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow|
346 | |[pytorch-semseg](https://github.com/meetshah1995/pytorch-semseg)|2.7k|Semantic Segmentation Architectures Implemented in PyTorch|
347 | |[pygcn](https://github.com/tkipf/pygcn)|2.7k|Graph Convolutional Networks in PyTorch|
348 | |[deep-learning-book](https://github.com/rasbt/deep-learning-book)|2.7k|Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in …|
349 | |[pointnet](https://github.com/charlesq34/pointnet)|2.7k|PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation|
350 | |[CVPR2020-Code](https://github.com/amusi/CVPR2020-Code)|2.7k|CVPR 2020 论文开源项目合集|
351 | |[Detectron.pytorch](https://github.com/roytseng-tw/Detectron.pytorch)|2.7k|A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron we…|
352 | |[LSTM-Human-Activity-Recognition](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition)|2.6k|Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier|
353 | |[neural-style-tf](https://github.com/cysmith/neural-style-tf)|2.6k|TensorFlow (Python API) implementation of Neural Style|
354 | |[Pytorch-UNet](https://github.com/milesial/Pytorch-UNet)|2.6k|PyTorch implementation of the U-Net for image semantic segmentation with high quality images|
355 | |[kornia](https://github.com/kornia/kornia)|2.6k|Open Source Differentiable Computer Vision Library for PyTorch|
356 | |[Ad-papers](https://github.com/wzhe06/Ad-papers)|2.6k|Papers on Computational Advertising|
357 | |[deep-high-resolution-net.pytorch](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch)|2.6k|The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Hum…|
358 | |[VoTT](https://github.com/microsoft/VoTT)|2.6k|Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.|
359 | |[espnet](https://github.com/espnet/espnet)|2.6k|End-to-End Speech Processing Toolkit|
360 | |[ltp](https://github.com/HIT-SCIR/ltp)|2.6k|Language Technology Platform|
361 | |[Learn_Deep_Learning_in_6_Weeks](https://github.com/llSourcell/Learn_Deep_Learning_in_6_Weeks)|2.6k|This is the Curriculum for "Learn Deep Learning in 6 Weeks" by Siraj Raval on Youtube|
362 | |[keras-vis](https://github.com/raghakot/keras-vis)|2.6k|Neural network visualization toolkit for keras|
363 | |[onnxruntime](https://github.com/microsoft/onnxruntime)|2.6k|ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator|
364 | |[3DDFA](https://github.com/cleardusk/3DDFA)|2.6k|The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.|
365 | |[olivia](https://github.com/olivia-ai/olivia)|2.6k|💁♀️Your new best friend powered by an artificial neural network|
366 | |[albert_zh](https://github.com/brightmart/albert_zh)|2.6k|A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS, 海量中文预训练ALBERT模型|
367 | |[ai-deadlines](https://github.com/abhshkdz/ai-deadlines)|2.6k|⏰ AI conference deadline countdowns|
368 | |[opencvsharp](https://github.com/shimat/opencvsharp)|2.5k|.NET Framework wrapper for OpenCV|
369 | |[telegram-list](https://github.com/goq/telegram-list)|2.5k|List of telegram groups, channels & bots // Список интересных групп, каналов и ботов телеграма // Список чатов для программистов|
370 | |[easy12306](https://github.com/zhaipro/easy12306)|2.5k|使用机器学习算法完成对12306验证码的自动识别|
371 | |[rust](https://github.com/tensorflow/rust)|2.5k|Rust language bindings for TensorFlow|
372 | |[miles-deep](https://github.com/ryanjay0/miles-deep)|2.5k|Deep Learning Porn Video Classifier/Editor with Caffe|
373 | |[VisualDL](https://github.com/PaddlePaddle/VisualDL)|2.5k|Deep Learning Visualization Toolkit（『飞桨』深度学习可视化工具 ）|
374 | |[SinGAN](https://github.com/tamarott/SinGAN)|2.5k|Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"|
375 | |[t81_558_deep_learning](https://github.com/jeffheaton/t81_558_deep_learning)|2.5k|Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks|
376 | |[training](https://github.com/cloud-annotations/training)|2.5k|🐝 Custom Object Detection and Classification Training|
377 | |[PocketFlow](https://github.com/Tencent/PocketFlow)|2.5k|An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.|
378 | |[autogluon](https://github.com/awslabs/autogluon)|2.5k|AutoGluon: AutoML Toolkit for Deep Learning|
379 | |[simple-faster-rcnn-pytorch](https://github.com/chenyuntc/simple-faster-rcnn-pytorch)|2.5k|A simplified implemention of Faster R-CNN that replicate performance from origin paper|
380 | |[MITIE](https://github.com/mit-nlp/MITIE)|2.5k|MITIE: library and tools for information extraction|
381 | |[reinforcement-learning](https://github.com/rlcode/reinforcement-learning)|2.5k|Minimal and Clean Reinforcement Learning Examples|
382 | |[ISLR-python](https://github.com/JWarmenhoven/ISLR-python)|2.4k|An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code|
383 | |[EAST](https://github.com/argman/EAST)|2.4k|A tensorflow implementation of EAST text detector|
384 | |[DeepNLP-models-Pytorch](https://github.com/DSKSD/DeepNLP-models-Pytorch)|2.4k|Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)|
385 | |[Machine-Learning-with-Python](https://github.com/susanli2016/Machine-Learning-with-Python)|2.4k|Python code for common Machine Learning Algorithms|
386 | |[stanford_dl_ex](https://github.com/amaas/stanford_dl_ex)|2.4k|Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial|
387 | |[tensorflow-internals](https://github.com/horance-liu/tensorflow-internals)|2.4k|It is open source ebook about TensorFlow kernel and implementation mechanism.|
388 | |[DeepLearning](https://github.com/MingchaoZhu/DeepLearning)|2.4k|Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现|
389 | |[text](https://github.com/pytorch/text)|2.4k|Data loaders and abstractions for text and NLP|
390 | |[ALAE](https://github.com/podgorskiy/ALAE)|2.4k|[CVPR2020] Adversarial Latent Autoencoders|
391 | |[pytorch-summary](https://github.com/sksq96/pytorch-summary)|2.4k|Model summary in PyTorch similar to `model.summary()` in Keras|
392 | |[pytorch-doc-zh](https://github.com/apachecn/pytorch-doc-zh)|2.4k|Pytorch 中文文档|
393 | |[Deep_reinforcement_learning_Course](https://github.com/simoninithomas/Deep_reinforcement_learning_Course)|2.4k|Implementations from the free course Deep Reinforcement Learning with Tensorflow|
394 | |[ML-Tutorial-Experiment](https://github.com/jiqizhixin/ML-Tutorial-Experiment)|2.4k|Coding the Machine Learning Tutorial for Learning to Learn|
395 | |[pytorch-Deep-Learning](https://github.com/Atcold/pytorch-Deep-Learning)|2.4k|Deep Learning (with PyTorch)|
396 | |[models](https://github.com/onnx/models)|2.4k|A collection of pre-trained, state-of-the-art models in the ONNX format|
397 | |[book](https://github.com/PaddlePaddle/book)|2.4k|Deep Learning 101 with PaddlePaddle （『飞桨』深度学习框架入门教程）|
398 | |[PyTorch_Tutorial](https://github.com/TingsongYu/PyTorch_Tutorial)|2.4k|《Pytorch模型训练实用教程》中配套代码|
399 | |[Dive-into-DL-TensorFlow2.0](https://github.com/TrickyGo/Dive-into-DL-TensorFlow2.0)|2.4k|本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意|
400 | |[Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials](https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials)|2.4k|A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding in…|
401 | |[segmentation_models](https://github.com/qubvel/segmentation_models)|2.4k|Segmentation models with pretrained backbones. Keras and TensorFlow Keras.|
402 | |[Awesome-PyTorch-Chinese](https://github.com/INTERMT/Awesome-PyTorch-Chinese)|2.3k|【干货】史上最全的PyTorch学习资源汇总|
403 | |[Flux.jl](https://github.com/FluxML/Flux.jl)|2.3k|Relax! Flux is the ML library that doesn't make you tensor|
404 | |[weld](https://github.com/weld-project/weld)|2.3k|High-performance runtime for data analytics applications|
405 | |[PyTorch-BigGraph](https://github.com/facebookresearch/PyTorch-BigGraph)|2.3k|Generate embeddings from large-scale graph-structured data.|
406 | |[byteps](https://github.com/bytedance/byteps)|2.3k|A high performance and generic framework for distributed DNN training|
407 | |[AI-Job-Notes](https://github.com/amusi/AI-Job-Notes)|2.3k|AI算法岗求职攻略（涵盖准备攻略、刷题指南、内推和AI公司清单等资料）|
408 | |[luminoth](https://github.com/tryolabs/luminoth)|2.3k|⚠️ UNMAINTAINED. Deep Learning toolkit for Computer Vision.|
409 | |[Alink](https://github.com/alibaba/Alink)|2.3k|Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platf…|
410 | |[introtodeeplearning](https://github.com/aamini/introtodeeplearning)|2.3k|Lab Materials for MIT 6.S191: Introduction to Deep Learning|
411 | |[TensorFlow-and-DeepLearning-Tutorial](https://github.com/CreatCodeBuild/TensorFlow-and-DeepLearning-Tutorial)|2.3k|A TensorFlow & Deep Learning online course I taught in 2016|
412 | |[srgan](https://github.com/tensorlayer/srgan)|2.3k|Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network|
413 | |[colorization](https://github.com/richzhang/colorization)|2.3k|Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.|
414 | |[OpenNMT](https://github.com/OpenNMT/OpenNMT)|2.3k|Open Source Neural Machine Translation in Torch (deprecated)|
415 | |[Super-SloMo](https://github.com/avinashpaliwal/Super-SloMo)|2.3k|PyTorch implementation of Super SloMo by Jiang et al.|
416 | |[orange3](https://github.com/biolab/orange3)|2.3k|🍊 📊 💡 Orange: Interactive data analysis https://orange.biolab.si|
417 | |[ENAS-pytorch](https://github.com/carpedm20/ENAS-pytorch)|2.3k|PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"|
418 | |[3D-ResNets-PyTorch](https://github.com/kenshohara/3D-ResNets-PyTorch)|2.3k|3D ResNets for Action Recognition (CVPR 2018)|
419 | |[pomegranate](https://github.com/jmschrei/pomegranate)|2.3k|Fast, flexible and easy to use probabilistic modelling in Python.|
420 | |[Faster-RCNN_TF](https://github.com/smallcorgi/Faster-RCNN_TF)|2.3k|Faster-RCNN in Tensorflow|
421 | |[datascience](https://github.com/r0f1/datascience)|2.3k|Curated list of Python resources for data science.|
422 | |[deep-learning-from-scratch](https://github.com/oreilly-japan/deep-learning-from-scratch)|2.3k|『ゼロから作る Deep Learning』(O'Reilly Japan, 2016)|
423 | |[covid-chestxray-dataset](https://github.com/ieee8023/covid-chestxray-dataset)|2.2k|We are building an open database of COVID-19 cases with chest X-ray or CT images.|
424 | |[deepchem](https://github.com/deepchem/deepchem)|2.2k|Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology|
425 | |[awesome-learning-resources](https://github.com/lauragift21/awesome-learning-resources)|2.2k|🔥 Awesome list of resources on Web Development.|
426 | |[omniscidb](https://github.com/omnisci/omniscidb)|2.2k|OmniSciDB (formerly MapD Core)|
427 | |[tablesaw](https://github.com/jtablesaw/tablesaw)|2.2k|Java dataframe and visualization library|
428 | |[Semantic-Segmentation-Suite](https://github.com/GeorgeSeif/Semantic-Segmentation-Suite)|2.2k|Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!|
429 | |[FCOS](https://github.com/tianzhi0549/FCOS)|2.2k|FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)|
430 | |[spotlight](https://github.com/maciejkula/spotlight)|2.2k|Deep recommender models using PyTorch.|
431 | |[datasets](https://github.com/tensorflow/datasets)|2.2k|TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...|
432 | |[Deep-Learning-for-Recommendation-Systems](https://github.com/robi56/Deep-Learning-for-Recommendation-Systems)|2.2k|This repository contains Deep Learning based articles , paper and repositories for Recommender Systems|
433 | |[gluon-nlp](https://github.com/dmlc/gluon-nlp)|2.1k|NLP made easy|
434 | |[dowhy](https://github.com/microsoft/dowhy)|2.1k|DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.|
435 | |[keras-attention-mechanism](https://github.com/philipperemy/keras-attention-mechanism)|2.1k|Attention mechanism Implementation for Keras.|
436 | |[Meta-Learning-Papers](https://github.com/floodsung/Meta-Learning-Papers)|2.1k|Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning|
437 | |[tacotron](https://github.com/keithito/tacotron)|2.1k|A TensorFlow implementation of Google's Tacotron speech synthesis with pre-trained model (unofficial)|
438 | |[machinelearninginaction](https://github.com/pbharrin/machinelearninginaction)|2.1k|Source Code for the book: Machine Learning in Action published by Manning|
439 | |[BuildingMachineLearningSystemsWithPython](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython)|2.1k|Source Code for the book Building Machine Learning Systems with Python|
440 | |[CHINESE-OCR](https://github.com/xiaofengShi/CHINESE-OCR)|2.1k|[python3.6] 运用tf实现自然场景文字检测,keras/pytorch实现ctpn+crnn+ctc实现不定长场景文字OCR识别|
441 | |[deepdetect](https://github.com/jolibrain/deepdetect)|2.1k|Deep Learning API and Server in C++11 support for Caffe, Caffe2, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost an…|
442 | |[XLM](https://github.com/facebookresearch/XLM)|2.1k|PyTorch original implementation of Cross-lingual Language Model Pretraining.|
443 | |[tensorflow-on-raspberry-pi](https://github.com/samjabrahams/tensorflow-on-raspberry-pi)|2.1k|TensorFlow for Raspberry Pi|
444 | |[decaNLP](https://github.com/salesforce/decaNLP)|2.1k|The Natural Language Decathlon: A Multitask Challenge for NLP|
445 | |[AlphaZero_Gomoku](https://github.com/junxiaosong/AlphaZero_Gomoku)|2.1k|An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)|
446 | |[pytorch-beginner](https://github.com/L1aoXingyu/pytorch-beginner)|2.1k|pytorch tutorial for beginners|
447 | |[tangent](https://github.com/google/tangent)|2.1k|Source-to-Source Debuggable Derivatives in Pure Python|
448 | |[Person_reID_baseline_pytorch](https://github.com/layumi/Person_reID_baseline_pytorch)|2.1k|A tiny, friendly, strong pytorch implement of person re-identification baseline. Tutorial 👉https://github.com/layumi/…|
449 | |[mmlspark](https://github.com/Azure/mmlspark)|2k|Microsoft Machine Learning for Apache Spark|
450 | |[FATE](https://github.com/FederatedAI/FATE)|2k|An Industrial Level Federated Learning Framework|
451 | |[text-to-image](https://github.com/paarthneekhara/text-to-image)|2k|Text to image synthesis using thought vectors|
452 | |[pytorch-sentiment-analysis](https://github.com/bentrevett/pytorch-sentiment-analysis)|2k|Tutorials on getting started with PyTorch and TorchText for sentiment analysis.|
453 | |[ELF](https://github.com/facebookresearch/ELF)|2k|An End-To-End, Lightweight and Flexible Platform for Game Research|
454 | |[catalyst](https://github.com/catalyst-team/catalyst)|2k|Accelerated DL R&D|
455 | |[neuralcoref](https://github.com/huggingface/neuralcoref)|2k|✨Fast Coreference Resolution in spaCy with Neural Networks|
456 | |[DeepRL-Agents](https://github.com/awjuliani/DeepRL-Agents)|2k|A set of Deep Reinforcement Learning Agents implemented in Tensorflow.|
457 | |[RL-Adventure](https://github.com/higgsfield/RL-Adventure)|2k|Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchic…|
458 | |[fe4ml-zh](https://github.com/apachecn/fe4ml-zh)|2k|📖 [译] 面向机器学习的特征工程|
459 | |[lingvo](https://github.com/tensorflow/lingvo)|2k|Lingvo|
460 | |[pytorch-generative-model-collections](https://github.com/znxlwm/pytorch-generative-model-collections)|2k|Collection of generative models in Pytorch version.|
461 | |[ResNeSt](https://github.com/zhanghang1989/ResNeSt)|2k|ResNeSt: Split-Attention Networks|
462 | |[TensorFlow-Tutorials](https://github.com/golbin/TensorFlow-Tutorials)|2k|텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다|
463 | |[MUNIT](https://github.com/NVlabs/MUNIT)|2k|Multimodal Unsupervised Image-to-Image Translation|
464 | |[oneDNN](https://github.com/oneapi-src/oneDNN)|2k|oneAPI Deep Neural Network Library (oneDNN)|
465 | |[deepvariant](https://github.com/google/deepvariant)|2k|DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.|
466 | |[texar](https://github.com/asyml/texar)|2k|Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow|
467 | |[kaolin](https://github.com/NVIDIAGameWorks/kaolin)|2k|A PyTorch Library for Accelerating 3D Deep Learning Research|
468 | |[Clairvoyant](https://github.com/anfederico/Clairvoyant)|2k|Software designed to identify and monitor social/historical cues for short term stock movement|
469 | |[implicit](https://github.com/benfred/implicit)|2k|Fast Python Collaborative Filtering for Implicit Feedback Datasets|
470 | |[DeepRL](https://github.com/ShangtongZhang/DeepRL)|2k|Modularized Implementation of Deep RL Algorithms in PyTorch|
471 | |[lgo](https://github.com/yunabe/lgo)|2k|Interactive Go programming with Jupyter|
472 | |[kcws](https://github.com/koth/kcws)|2k|Deep Learning Chinese Word Segment|
473 | |[tensorflow-build-archived](https://github.com/lakshayg/tensorflow-build-archived)|2k|TensorFlow binaries supporting AVX, FMA, SSE|
474 | |[dm_control](https://github.com/deepmind/dm_control)|2k|DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.|
475 | |[gpytorch](https://github.com/cornellius-gp/gpytorch)|2k|A highly efficient and modular implementation of Gaussian Processes in PyTorch|
476 | |[Neural-Photo-Editor](https://github.com/ajbrock/Neural-Photo-Editor)|1.9k|A simple interface for editing natural photos with generative neural networks.|
477 | |[alpha-zero-general](https://github.com/suragnair/alpha-zero-general)|1.9k|A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4|
478 | |[tacotron2](https://github.com/NVIDIA/tacotron2)|1.9k|Tacotron 2 - PyTorch implementation with faster-than-realtime inference|
479 | |[siamese-triplet](https://github.com/adambielski/siamese-triplet)|1.9k|Siamese and triplet networks with online pair/triplet mining in PyTorch|
480 | |[awesome-quant](https://github.com/thuquant/awesome-quant)|1.9k|中国的Quant相关资源索引|
481 | |[image-super-resolution](https://github.com/idealo/image-super-resolution)|1.9k|🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.|
482 | |[generative_inpainting](https://github.com/JiahuiYu/generative_inpainting)|1.9k|DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral|
483 | |[code-of-learn-deep-learning-with-pytorch](https://github.com/L1aoXingyu/code-of-learn-deep-learning-with-pytorch)|1.9k|This is code of book "Learn Deep Learning with PyTorch"|
484 | |[gpt-2-simple](https://github.com/minimaxir/gpt-2-simple)|1.9k|Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts|
485 | |[DeepInterests](https://github.com/Honlan/DeepInterests)|1.9k|深度有趣|
486 | |[segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch)|1.9k|Segmentation models with pretrained backbones. PyTorch.|
487 | |[human-pose-estimation.pytorch](https://github.com/microsoft/human-pose-estimation.pytorch)|1.9k|The project is an official implement of our ECCV2018 paper "Simple Baselines for Human Pose Estimation and Tracking(h…|
488 | |[BigGAN-PyTorch](https://github.com/ajbrock/BigGAN-PyTorch)|1.9k|The author's officially unofficial PyTorch BigGAN implementation.|
489 | |[pytorch-playground](https://github.com/aaron-xichen/pytorch-playground)|1.9k|Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)|
490 | |[bertviz](https://github.com/jessevig/bertviz)|1.9k|Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)|
491 | |[face.evoLVe.PyTorch](https://github.com/ZhaoJ9014/face.evoLVe.PyTorch)|1.9k|🔥🔥High-Performance Face Recognition Library on PyTorch🔥🔥|
492 | |[Reco-papers](https://github.com/wzhe06/Reco-papers)|1.8k|Classic papers and resources on recommendation|
493 | |[coach](https://github.com/NervanaSystems/coach)|1.8k|Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms|
494 | |[sling](https://github.com/google/sling)|1.8k|SLING - A natural language frame semantics parser|
495 | |[pytorch-deeplab-xception](https://github.com/jfzhang95/pytorch-deeplab-xception)|1.8k|DeepLab v3+ model in PyTorch. Support different backbones.|
496 | |[mmskeleton](https://github.com/open-mmlab/mmskeleton)|1.8k|A OpenMMLAB toolbox for human pose estimation, skeleton-based action recognition, and action synthesis.|
497 | |[sru](https://github.com/asappresearch/sru)|1.8k|Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)|
498 | |[pytorch-seq2seq](https://github.com/bentrevett/pytorch-seq2seq)|1.8k|Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.|
499 | |[Deep-Learning-Interview-Book](https://github.com/amusi/Deep-Learning-Interview-Book)|1.8k|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）|
500 | |[pai](https://github.com/microsoft/pai)|1.8k|Resource scheduling and cluster management for AI|
501 | |[AI-Blocks](https://github.com/MrNothing/AI-Blocks)|1.8k|A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models!|
502 | |[scikit-optimize](https://github.com/scikit-optimize/scikit-optimize)|1.8k|Sequential model-based optimization with a `scipy.optimize` interface|
503 | |[sequence_tagging](https://github.com/guillaumegenthial/sequence_tagging)|1.8k|Named Entity Recognition (LSTM + CRF) - Tensorflow|
504 | |[zh-NER-TF](https://github.com/Determined22/zh-NER-TF)|1.8k|A very simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (TensorFlow)|
505 | |[donkeycar](https://github.com/autorope/donkeycar)|1.8k|Open source hardware and software platform to build a small scale self driving car.|
506 | |[edge-connect](https://github.com/knazeri/edge-connect)|1.8k|EdgeConnect: Structure Guided Image Inpainting using Edge Prediction, ICCV 2019 https://arxiv.org/abs/1901.00212|
507 | |[awd-lstm-lm](https://github.com/salesforce/awd-lstm-lm)|1.7k|LSTM and QRNN Language Model Toolkit for PyTorch|
508 | |[pytorch-kaldi](https://github.com/mravanelli/pytorch-kaldi)|1.7k|pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.|
509 | |[Bender](https://github.com/xmartlabs/Bender)|1.7k|Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.|
510 | |[zi2zi](https://github.com/kaonashi-tyc/zi2zi)|1.7k|Learning Chinese Character style with conditional GAN|
511 | |[automl-gs](https://github.com/minimaxir/automl-gs)|1.7k|Provide an input CSV and a target field to predict, generate a model + code to run it.|
512 | |[stats](https://github.com/montanaflynn/stats)|1.7k|A well tested and comprehensive Golang statistics library package with no dependencies.|
513 | |[ranking](https://github.com/tensorflow/ranking)|1.7k|Learning to Rank in TensorFlow|
514 | |[mathAI](https://github.com/Roujack/mathAI)|1.7k|一个拍照做题程序。输入一张包含数学计算题的图片，输出识别出的数学计算式以及计算结果。This is a mathematic expression recognition project.|
515 | |[spark-ml-source-analysis](https://github.com/endymecy/spark-ml-source-analysis)|1.7k|spark ml 算法原理剖析以及具体的源码实现分析|
516 | |[video-object-removal](https://github.com/zllrunning/video-object-removal)|1.7k|Just draw a bounding box and you can remove the object you want to remove.|
517 | |[datascience-pizza](https://github.com/PizzaDeDados/datascience-pizza)|1.7k|🍕 Repositório para juntar informações sobre materiais de estudo em análise de dados e áreas afins, empresas que trabalham com dados e dicionário de conceitos|
518 | |[data-science-interviews](https://github.com/alexeygrigorev/data-science-interviews)|1.7k|Data science interview questions and answers|
519 | |[yolov3-tf2](https://github.com/zzh8829/yolov3-tf2)|1.7k|YoloV3 Implemented in Tensorflow 2.0|
520 | |[ComputeLibrary](https://github.com/ARM-software/ComputeLibrary)|1.7k|The ARM Computer Vision and Machine Learning library is a set of functions optimised for both ARM CPUs and GPUs using SIMD technologies.|
521 | |[tacotron](https://github.com/Kyubyong/tacotron)|1.7k|A TensorFlow Implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model|
522 | |[DeepLearn](https://github.com/GauravBh1010tt/DeepLearn)|1.7k|Implementation of research papers on Deep Learning+ NLP+ CV in Python using Keras, Tensorflow and Scikit Learn.|
523 | |[analytics-zoo](https://github.com/intel-analytics/analytics-zoo)|1.7k|Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray|
524 | |[PyTorch-NLP](https://github.com/PetrochukM/PyTorch-NLP)|1.7k|Basic Utilities for PyTorch Natural Language Processing (NLP)|
525 | |[captcha_break](https://github.com/ypwhs/captcha_break)|1.7k|验证码识别|
526 | |[crnn](https://github.com/bgshih/crnn)|1.7k|Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition.|
527 | |[DeblurGAN](https://github.com/KupynOrest/DeblurGAN)|1.7k|Image Deblurring using Generative Adversarial Networks|
528 | |[robosat](https://github.com/mapbox/robosat)|1.6k|Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds|
529 | |[pointnet2](https://github.com/charlesq34/pointnet2)|1.6k|PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space|
530 | |[AutonomousDrivingCookbook](https://github.com/microsoft/AutonomousDrivingCookbook)|1.6k|Scenarios, tutorials and demos for Autonomous Driving|
531 | |[imgclsmob](https://github.com/osmr/imgclsmob)|1.6k|Sandbox for training convolutional networks for computer vision|
532 | |[tf_unet](https://github.com/jakeret/tf_unet)|1.6k|Generic U-Net Tensorflow implementation for image segmentation|
533 | |[torchsample](https://github.com/ncullen93/torchsample)|1.6k|High-Level Training, Data Augmentation, and Utilities for Pytorch|
534 | |[nlp](https://github.com/huggingface/nlp)|1.6k|🤗nlp – Datasets and evaluation metrics for Natural Language Processing in NumPy, Pandas, PyTorch and TensorFlow|
535 | |[hdbscan](https://github.com/scikit-learn-contrib/hdbscan)|1.6k|A high performance implementation of HDBSCAN clustering.|
536 | |[m2cgen](https://github.com/BayesWitnesses/m2cgen)|1.6k|Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby) with zero dependencies|
537 | |[fastNLP](https://github.com/fastnlp/fastNLP)|1.6k|fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.|
538 | |[keras-yolo2](https://github.com/experiencor/keras-yolo2)|1.6k|Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).|
539 | |[Awesome-Chatbot](https://github.com/fendouai/Awesome-Chatbot)|1.6k|Awesome Chatbot Projects,Corpus,Papers,Tutorials.Chinese Chatbot =>:|
540 | |[knockknock](https://github.com/huggingface/knockknock)|1.6k|🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code|
541 | |[MTBook](https://github.com/NiuTrans/MTBook)|1.6k|《机器翻译：统计建模与深度学习方法》肖桐 朱靖波 著 - Machine Translation: Statistical Modeling and Deep Learning Methods|
542 | |[trains](https://github.com/allegroai/trains)|1.6k|TRAINS - Auto-Magical Experiment Manager & Version Control for AI - NOW WITH AUTO-MAGICAL DEVOPS!|
543 | |[self-driving-car](https://github.com/ndrplz/self-driving-car)|1.6k|Udacity Self-Driving Car Engineer Nanodegree projects.|
544 | |[cnn_captcha](https://github.com/nickliqian/cnn_captcha)|1.6k|use cnn recognize captcha by tensorflow. 本项目针对字符型图片验证码，使用tensorflow实现卷积神经网络，进行验证码识别。|
545 | |[XLearning](https://github.com/Qihoo360/XLearning)|1.6k|AI on Hadoop|
546 | |[Tacotron-2](https://github.com/Rayhane-mamah/Tacotron-2)|1.6k|DeepMind's Tacotron-2 Tensorflow implementation|
547 | |[fast-wavenet](https://github.com/tomlepaine/fast-wavenet)|1.6k|Speedy Wavenet generation using dynamic programming ⚡|
548 | |[spacy-course](https://github.com/ines/spacy-course)|1.6k|👩🏫 Advanced NLP with spaCy: A free online course|
549 | |[gandissect](https://github.com/CSAILVision/gandissect)|1.6k|Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/|
550 | |[NCRFpp](https://github.com/jiesutd/NCRFpp)|1.6k|NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.|
551 | |[stargan-v2](https://github.com/clovaai/stargan-v2)|1.6k|StarGAN v2 - Official PyTorch Implementation (CVPR 2020)|
552 | |[tinyflow](https://github.com/tqchen/tinyflow)|1.6k|Tutorial code on how to build your own Deep Learning System in 2k Lines|
553 | |[UNIT](https://github.com/mingyuliutw/UNIT)|1.6k|Unsupervised Image-to-Image Translation|
554 | |[ssd_keras](https://github.com/pierluigiferrari/ssd_keras)|1.6k|A Keras port of Single Shot MultiBox Detector|
555 | |[cosin](https://github.com/chatopera/cosin)|1.5k|🌲 春松客服，多渠道智能客服系统，开源客服系统|
556 | |[foolbox](https://github.com/bethgelab/foolbox)|1.5k|A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX|
557 | |[capsule-networks](https://github.com/gram-ai/capsule-networks)|1.5k|A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".|
558 | |[flogo](https://github.com/TIBCOSoftware/flogo)|1.5k|Project Flogo is an open source ecosystem of opinionated event-driven capabilities to simplify building efficient & modern serverless functions, microservices & edge apps.|
559 | |[lip-reading-deeplearning](https://github.com/astorfi/lip-reading-deeplearning)|1.5k|🔓 Lip Reading - Cross Audio-Visual Recognition using 3D Architectures|
560 | |[hummingbird](https://github.com/microsoft/hummingbird)|1.5k|Hummingbird compiles trained ML models into tensor computation for faster inference.|
561 | |[deep-rl-tensorflow](https://github.com/carpedm20/deep-rl-tensorflow)|1.5k|TensorFlow implementation of Deep Reinforcement Learning papers|
562 | |[practical-machine-learning-with-python](https://github.com/dipanjanS/practical-machine-learning-with-python)|1.5k|Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.|
563 | |[NeuroNER](https://github.com/Franck-Dernoncourt/NeuroNER)|1.5k|Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.|
564 | |[wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder)|1.5k|WaveNet vocoder|
565 | |[awesome-hand-pose-estimation](https://github.com/xinghaochen/awesome-hand-pose-estimation)|1.5k|Awesome work on hand pose estimation/tracking|
566 | |[mAP](https://github.com/Cartucho/mAP)|1.5k|mean Average Precision - This code evaluates the performance of your neural net for object recognition.|
567 | |[agents](https://github.com/tensorflow/agents)|1.5k|TF-Agents is a library for Reinforcement Learning in TensorFlow|
568 | |[CADL](https://github.com/pkmital/CADL)|1.5k|ARCHIVED: Contains historical course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL|
569 | |[tensorflow-DeepFM](https://github.com/ChenglongChen/tensorflow-DeepFM)|1.5k|Tensorflow implementation of DeepFM for CTR prediction.|
570 | |[tensorflow-1.4-billion-password-analysis](https://github.com/philipperemy/tensorflow-1.4-billion-password-analysis)|1.5k|Deep Learning model to analyze a large corpus of clear text passwords.|
571 | |[DAT8](https://github.com/justmarkham/DAT8)|1.5k|General Assembly's 2015 Data Science course in Washington, DC|
572 | |[NeMo](https://github.com/NVIDIA/NeMo)|1.5k|NeMo: a toolkit for conversational AI|
573 | |[Machine-Learning-Flappy-Bird](https://github.com/ssusnic/Machine-Learning-Flappy-Bird)|1.5k|Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm|
574 | |[ml-visuals](https://github.com/dair-ai/ml-visuals)|1.5k|Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.|
575 | |[GANimation](https://github.com/albertpumarola/GANimation)|1.5k|GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]|
576 | |[EagleEye](https://github.com/ThoughtfulDev/EagleEye)|1.5k|Stalk your Friends. Find their Instagram, FB and Twitter Profiles using Image Recognition and Reverse Image Search.|
577 | |[PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding)|1.5k|A PyTorch CV Toolkit|
578 | |[spark](https://github.com/dotnet/spark)|1.5k|.NET for Apache® Spark™ makes Apache Spark™ easily accessible to .NET developers.|
579 | |[quiver](https://github.com/keplr-io/quiver)|1.5k|Interactive convnet features visualization for Keras|
580 | |[MachineLearning](https://github.com/jindongwang/MachineLearning)|1.5k|一些关于机器学习的学习资料与研究介绍|
581 | |[GAT](https://github.com/PetarV-/GAT)|1.5k|Graph Attention Networks (https://arxiv.org/abs/1710.10903)|
582 | |[mt-dnn](https://github.com/namisan/mt-dnn)|1.4k|Multi-Task Deep Neural Networks for Natural Language Understanding|
583 | |[deep-neuroevolution](https://github.com/uber-research/deep-neuroevolution)|1.4k|Deep Neuroevolution|
584 | |[a-PyTorch-Tutorial-to-Object-Detection](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection)|1.4k|SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection|
585 | |[labelbox](https://github.com/Labelbox/labelbox)|1.4k|Labelbox is the fastest way to annotate data to build and ship computer vision applications.|
586 | |[openvino](https://github.com/openvinotoolkit/openvino)|1.4k|OpenVINO™ Toolkit repository|
587 | |[awesome-decision-tree-papers](https://github.com/benedekrozemberczki/awesome-decision-tree-papers)|1.4k|A collection of research papers on decision, classification and regression trees with implementations.|
588 | |[project_alias](https://github.com/bjoernkarmann/project_alias)|1.4k|Alias is a teachable “parasite” that is designed to give users more control over their smart assistants, both when it comes to customisation and privacy. Through a simple app the user can train Alias to react on a custom wake-word/sound, and once trained, Alias can take control over your home assistant by activating it for you.|
589 | |[data-science-question-answer](https://github.com/ShuaiW/data-science-question-answer)|1.4k|A repo for data science related questions and answers|
590 | |[photo2cartoon](https://github.com/minivision-ai/photo2cartoon)|1.4k|人像卡通化探索项目 (photo-to-cartoon translation project)|
591 | |[video2x](https://github.com/k4yt3x/video2x)|1.4k|A lossless video/GIF/image upscaler achieved with waifu2x, Anime4K, SRMD and RealSR. Started in Hack the Valley 2, 2018.|
592 | |[Tengine](https://github.com/OAID/Tengine)|1.4k|Tengine is a lite, high performance, modular inference engine for embedded device|
593 | |[cuml](https://github.com/rapidsai/cuml)|1.4k|cuML - RAPIDS Machine Learning Library|
594 | |[BentoML](https://github.com/bentoml/BentoML)|1.4k|Model Serving Made Easy|
595 | |[GANotebooks](https://github.com/tjwei/GANotebooks)|1.4k|wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch|
596 | |[MobileNet](https://github.com/Zehaos/MobileNet)|1.4k|MobileNet build with Tensorflow|
597 | |[CRAFT-pytorch](https://github.com/clovaai/CRAFT-pytorch)|1.4k|Official implementation of Character Region Awareness for Text Detection (CRAFT)|
598 | |[mlr](https://github.com/mlr-org/mlr)|1.4k|Machine Learning in R|
599 | |[monodepth2](https://github.com/nianticlabs/monodepth2)|1.4k|Monocular depth estimation from a single image|
600 | |[TensorKart](https://github.com/kevinhughes27/TensorKart)|1.4k|self-driving MarioKart with TensorFlow|
601 | |[keras-contrib](https://github.com/keras-team/keras-contrib)|1.4k|Keras community contributions|
602 | |[stellargraph](https://github.com/stellargraph/stellargraph)|1.4k|StellarGraph - Machine Learning on Graphs|
603 | |[GDLnotes](https://github.com/ahangchen/GDLnotes)|1.4k|Google Deep Learning Notes（TensorFlow教程）|
604 | |[pydensecrf](https://github.com/lucasb-eyer/pydensecrf)|1.4k|Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.|
605 | |[seldon-server](https://github.com/SeldonIO/seldon-server)|1.4k|Machine Learning Platform and Recommendation Engine built on Kubernetes|
606 | |[chainercv](https://github.com/chainer/chainercv)|1.4k|ChainerCV: a Library for Deep Learning in Computer Vision|
607 | |[tensorflow-nlp](https://github.com/zhedongzheng/tensorflow-nlp)|1.4k|Building blocks for NLP and Text Generation in TensorFlow 2.x / 1.x|
608 | |[iOS_ML](https://github.com/alexsosn/iOS_ML)|1.4k|List of Machine Learning, AI, NLP solutions for iOS. The most recent version of this article can be found on my blog.|
609 | |[tfgo](https://github.com/galeone/tfgo)|1.4k|Tensorflow + Go, the gopher way|
610 | |[bi-att-flow](https://github.com/allenai/bi-att-flow)|1.4k|Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that represents context at different levels of granularity and uses a bi-directional attention flow mechanism to achieve a query-aware context representation without early summarization.|
611 | |[CoreML-in-ARKit](https://github.com/hanleyweng/CoreML-in-ARKit)|1.4k|Simple project to detect objects and display 3D labels above them in AR. This serves as a basic Template for an ARKit project to use CoreML.|
612 | |[lightning](https://github.com/scikit-learn-contrib/lightning)|1.4k|Large-scale linear classification, regression and ranking in Python|
613 | |[DeepFace](https://github.com/RiweiChen/DeepFace)|1.4k|Face analysis mainly based on Caffe. At this time, face analysis tasks like detection, alignment and recognition have been done.|
614 | |[torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt)|1.4k|An easy to use PyTorch to TensorRT converter|
615 | |[deepvoice3_pytorch](https://github.com/r9y9/deepvoice3_pytorch)|1.4k|PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models|
616 | |[sphereface](https://github.com/wy1iu/sphereface)|1.4k|Implementation for <SphereFace: Deep Hypersphere Embedding for Face Recognition> in CVPR'17.|
617 | |[jeelizFaceFilter](https://github.com/jeeliz/jeelizFaceFilter)|1.4k|Javascript/WebGL lightweight face tracking library designed for augmented reality webcam filters. Features : multiple faces detection, rotation, mouth opening. Various integration examples are provided (Three.js, Babylon.js, FaceSwap, Canvas2D, CSS3D...).|
618 | |[kaggle-web-traffic](https://github.com/Arturus/kaggle-web-traffic)|1.4k|1st place solution|
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751 |
752 |
--------------------------------------------------------------------------------
/scripts/generate_stats.py:
--------------------------------------------------------------------------------
1 | from bs4 import BeautifulSoup
2 | import math
3 | import operator
4 | import requests
5 | import time
6 |
7 | SKIP_LIST = ["awesome", "notebook", "learn", "curated list"]
8 |
9 | def search(keywords, n_pages=10, sort='stars'):
10 | res = []
11 | for k in keywords:
12 | for i in range(1, n_pages+1):
13 | time.sleep(20)
14 | url = "https://github.com/search?o=desc&q=%s&p=%d&s=%s&type=Repositories" % (k, i, sort)
15 | r = requests.get(url)
16 | info = extract_search_info(r.content)
17 | for r in info:
18 | res.append(r)
19 | return res
20 |
21 | def extract_search_info(html):
22 | info = []
23 | html = BeautifulSoup(html, 'html.parser')
24 | for c in html.find_all('li', {"class": "repo-list-item"}):
25 | url = None
26 | try:
27 | stars = str(c.find_all('a', {"class": "muted-link"})[-1].text.strip())
28 | stars_unparsed = stars
29 | if 'k' in stars:
30 | stars = float(stars.replace('k', '')) * 1000
31 | stars = int(stars)
32 | lang = None
33 | url = "https://github.com" + c.find('a', {"class": "v-align-middle"}).attrs["href"]
34 | title = c.find('a', {"class": "v-align-middle"}).text.strip().split('/')[-1]
35 | desc = c.find('p', {"class": "mb-1"}).text.strip()
36 | skip = 0
37 | for w in SKIP_LIST:
38 | if w in desc:
39 | skip = 1
40 | break
41 | if skip == 0:
42 | info.append({
43 | "url": url,
44 | "title": title,
45 | "desc": desc.strip(),
46 | "stars_unparsed": stars_unparsed,
47 | "stars": stars,
48 | "lang": lang
49 | })
50 | except Exception as e:
51 | print url
52 | print e
53 | return info
54 |
55 | def get_topic(keywords, n_pages=10):
56 | res = []
57 | next_token = None
58 | for k in keywords:
59 | for i in range(1, n_pages+1):
60 | time.sleep(20)
61 | url = "https://github.com/topics/%s?page=%i" % (k, i)
62 | r = requests.get(url)
63 | info = extract_topic_info(r.content)
64 | for r in info:
65 | res.append(r)
66 | return res
67 |
68 | def extract_topic_info(html):
69 | info = []
70 | html = BeautifulSoup(html, 'html.parser')
71 | for c in html.find_all('article', {"class": "my-4"}):
72 | url = None
73 | try:
74 | stars = str(c.find_all('a', {"class": "social-count"})[-1].text.strip())
75 | stars_unparsed = stars
76 | if 'k' in stars:
77 | stars = float(stars.replace('k', '')) * 1000
78 | stars = int(stars)
79 | lang = None
80 | url = "https://github.com" + c.find('h1').find_all('a')[1].attrs["href"]
81 | time.sleep(5)
82 | r = requests.get(url)
83 | desc = BeautifulSoup(r.content, 'html.parser').find('p', {'class': 'f4'}).text
84 | skip = 0
85 | for w in SKIP_LIST:
86 | if w in desc:
87 | skip = 1
88 | break
89 | if skip == 0:
90 | info.append({
91 | "url": url,
92 | "title": c.find('h1').find_all('a')[1].text.strip().replace(" / ", "/"),
93 | "desc": desc.strip(),
94 | "stars_unparsed": stars_unparsed,
95 | "stars": stars,
96 | "lang": lang
97 | })
98 | except Exception as e:
99 | print url
100 | print e
101 | return info
102 |
103 | def parse_results(results):
104 | results = {v['url']:v for v in results}.values()
105 | results = sorted(results, key=lambda x: x['stars'], reverse=True)
106 | return [r for r in results if r['stars'] >= 1000]
107 |
108 | def build_table(results_list):
109 |
110 | def build_html_fields(d):
111 | return ['<a href="%s">%s</a>' % (d['url'], d['title'].split('/')[-1]), d['stars_unparsed'], d['desc']]
112 |
113 | def build_md_fields(d):
114 | return ['[%s](%s)' % (d['title'].split('/')[-1], d['url']), d['stars_unparsed'], d['desc']]
115 |
116 | html = '<table><thead><tr><td>Project Name</td><td>Stars</td><td>Description</td></tr></thead>'
117 | md = '| Project Name | Stars | Description |\n| ------- | ------ | ------ |\n'
118 | for r in results_list:
119 | html += '<tr><td>' + '</td><td>'.join(build_html_fields(r)) + '</td></tr>'
120 | md += '|' + '|'.join(build_md_fields(r)) + '|\n'
121 | html += '</table>'
122 | return html, md
123 |
124 | topics = get_topic(['tensorflow', 'deep-learning', 'pytorch', 'machine-learning'], n_pages=15)
125 | searches = search(['tensorflow', 'deep learning', 'pytorch', 'cntk', 'machine learning'], n_pages=15)
126 |
127 | r = parse_results(topics + searches)
128 |
129 | print len(r)
130 |
131 | with open('out.html', 'w') as f:
132 | f.write(build_table(r)[0].encode('utf-8'))
133 |
134 | with open('out.md', 'w') as f:
135 | f.write(build_table(r)[1].encode('utf-8'))
136 |
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