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https://github.com/1044197988/Awesome-Tensorflow2

基于Tensorflow2开发的优秀扩展包及项目
https://github.com/1044197988/Awesome-Tensorflow2
automl awesome awesome-list classification detection-tensorflow2 gan keras nlp segmentation tensorflow tensorflow-examples tensorflow-experiments tensorflow-models tensorflow2 tensorflow2-awesome tf2 tutorials
Last synced: 3 months ago
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基于Tensorflow2开发的优秀扩展包及项目
Host: GitHub
URL: https://github.com/1044197988/Awesome-Tensorflow2
Owner: 1044197988
License: apache-2.0
Created: 2019-09-27T03:35:22.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2020-02-27T14:56:00.000Z (over 4 years ago)
Last Synced: 2024-08-02T19:01:28.868Z (3 months ago)
Topics: automl, awesome, awesome-list, classification, detection-tensorflow2, gan, keras, nlp, segmentation, tensorflow, tensorflow-examples, tensorflow-experiments, tensorflow-models, tensorflow2, tensorflow2-awesome, tf2, tutorials
Homepage:
Size: 130 KB
Stars: 47
Watchers: 7
Forks: 23
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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ultimate-awesome - Awesome-Tensorflow2 - 基于Tensorflow2开发的优秀扩展包及项目. (Programming Language Lists / Python Lists)
README

        # Awesome-Tensorflow2 💛 [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

基于Tensorflow2开发的优秀扩展包及项目

![Tensorflow](https://github.com/1044197988/Awesome-Tensorflow2/blob/master/Logo/Logo.jpg)

# Tensorflow2说明

2019 谷歌开发者大会于 9 月 10 日和 11 日在上海举办，大会将分享众多开发经验与工具。在第一天的 KeyNote 中，谷歌发布了很多开发工具新特性，并介绍而它们是如何构建更好的应用。值得注意的是，TensorFlow 刚刚发布了 2.0 RC01 版和 1.15，谷歌表示 1.15 是 1.x 的最后一次更新了。TensorFlow 2.0 相信大家已经非常熟悉了，它重点还是放在优化 Keras 和 Eager Execution 的能力，它希望通过这两种 API 简化整个开发流程。所以，未来的趋势肯定是Tensorflow2.0，在此我整合了许多的优秀库及项目到这个贡献库里。

# 提示

有些项目目前仍在进行中，将在未来支持Tensorflow2，这样的项目也包含在下面列表中。（意味着目前并不支持二版本）

# Contents 👈

* [Tutorials](#github-tutorials)

* [Model](#Model)

* [Projects](#Projects)

* [Other](#other)

 

## Tutorials 💛💛💛💛💛

* [tensorflow.google.cn](https://tensorflow.google.cn/beta)

* [dragen1860/TensorFlow-2.x-Tutorials](https://github.com/dragen1860/TensorFlow-2.x-Tutorials)

* [dragen1860/Deep-Learning-with-TensorFlow-book](https://github.com/dragen1860/Deep-Learning-with-TensorFlow-book)

* [czy36mengfei/tensorflow2_tutorials_chinese](https://github.com/czy36mengfei/tensorflow2_tutorials_chinese)

* [yusugomori/deeplearning-tf2](https://github.com/yusugomori/deeplearning-tf2)

* [YunYang1994/TensorFlow2.0-Examples](https://github.com/YunYang1994/TensorFlow2.0-Examples)

## Model 💛💛💛💛💛

### Classification 分类

* [tensorflow/models](https://github.com/tensorflow/models)


该存储库包含在TensorFlow中实现的许多不同模型。


* [1044197988/TF.Keras-Commonly-used-models](https://github.com/1044197988/TF.Keras-Commonly-used-models)


该贡献库为我整理的一些常用的分类、分割模型，包含分割的一些指标、损失函数，但不提供预训练模型的载入。分割模型列表如下：


![Segmentation](https://github.com/1044197988/Awesome-Tensorflow2/blob/master/Logo/2.png)

#### Large library 大型库

* [qubvel/classification_models](https://github.com/qubvel/classification_models#architectures)

#### Model 模型

* [qubvel/efficientnet](https://github.com/qubvel/efficientnet)

* [nsarang/MnasNet](https://github.com/nsarang/MnasNet)

* [calmisential/MobileNetV3_TensorFlow2](https://github.com/calmisential/MobileNetV3_TensorFlow2)

* [calmisential/TensorFlow2.0_ResNet](https://github.com/calmisential/TensorFlow2.0_ResNet)

* [calmisential/TensorFlow2.0_InceptionV3](https://github.com/calmisential/TensorFlow2.0_InceptionV3)

* [calmisential/TensorFlow2.0_Image_Classification](https://github.com/calmisential/TensorFlow2.0_Image_Classification)

##### Capsnet model 胶囊网络模型

* [prabhuomkar/hicr-capsnet](https://github.com/prabhuomkar/hicr-capsnet)

##### Semi-supervised learning 半监督学习

* [ntozer/mixmatch-tensorflow2.0](https://github.com/ntozer/mixmatch-tensorflow2.0)

* [schatty/prototypical-networks-tf](https://github.com/schatty/prototypical-networks-tf)

##### Generative-models and Self encoder 生成模型和自编码

* [timsainb/tensorflow2-generative-models](https://github.com/timsainb/tensorflow2-generative-models)

* [DequanZhu/GANs-collections-tf2.0_keras-eager_mode](https://github.com/DequanZhu/GANs-collections-tf2.0_keras-eager_mode)

* [Hourout/GAN-keras](https://github.com/Hourout/GAN-keras)

* [Net-Mist/style-transfer-tf2](https://github.com/Net-Mist/style-transfer-tf2)

* [mnicnc404/CartoonGan-tensorflow](https://github.com/mnicnc404/CartoonGan-tensorflow)

* [ialhashim/StyleGAN-Tensorflow2](https://github.com/ialhashim/StyleGAN-Tensorflow2)

* [leafinity/SAGAN-tensorflow2.0](https://github.com/leafinity/SAGAN-tensorflow2.0)

* [Lornatang/TensorFlow2-GAN](https://github.com/Lornatang/TensorFlow2-GAN)

* [ppooiiuuyh/SinGAN-tensorflow2.0](https://github.com/ppooiiuuyh/SinGAN-tensorflow2.0)

* [hollygrimm/tf2-cyclegan](https://github.com/hollygrimm/tf2-cyclegan)

* [drewszurko/tensorflow-WGAN-GP](https://github.com/drewszurko/tensorflow-WGAN-GP)

* [mgmk2/StyleGAN](https://github.com/mgmk2/StyleGAN)

* [LynnHo/CycleGAN-Tensorflow-2](https://github.com/LynnHo/CycleGAN-Tensorflow-2)

### Segmentation 分割

#### Large library 大型库

* [qubvel/segmentation_models](https://github.com/qubvel/segmentation_models)

#### Model 模型 

* [srihari-humbarwadi/FastFCN_TF2.0](https://github.com/srihari-humbarwadi/FastFCN_TF2.0)

* [bonlime/keras-deeplab-v3-plus](https://github.com/bonlime/keras-deeplab-v3-plus)

* [matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN)

* [srihari-humbarwadi/DeepLabV3_Plus-Tensorflow2.0](https://github.com/srihari-humbarwadi/DeepLabV3_Plus-Tensorflow2.0)

### Super resolution 超分辨率

#### Model 模型

* [krasserm/super-resolution](https://github.com/krasserm/super-resolution)


包含以下模型：


用于单图像超分辨率（EDSR）的增强型深度残留网络，是NTIRE 2017超分辨率挑战赛的冠军。


广泛激活以实现高效，准确的图像超分辨率（WDSR），是NTIRE 2018超分辨率挑战赛（真实轨道）的获胜者。


使用生成对抗网络（SRGAN）的逼真的单图像超分辨率。


* [HasnainRaz/Fast-SRGAN](https://github.com/HasnainRaz/Fast-SRGAN)

* [gs18113/ESPCN-TensorFlow2](https://github.com/gs18113/ESPCN-TensorFlow2)

### Object detection 目标检测

#### Model 模型

* [zzh8829/yolov3-tf2](https://github.com/zzh8829/yolov3-tf2)

* [srihari-humbarwadi/YOLOv1-TensorFlow2.0](https://github.com/srihari-humbarwadi/YOLOv1-TensorFlow2.0)

* [cjpurackal/m2det-tf](https://github.com/cjpurackal/m2det-tf)

* [reliefs/mtcnn-tensorflow2](https://github.com/reliefs/mtcnn-tensorflow2)

* [1044197988/Centernet-Tensorflow2.0](https://github.com/1044197988/Centernet-Tensorflow2.0)

* [calmisential/TensorFlow2.0_FasterRCNN](https://github.com/calmisential/TensorFlow2.0_FasterRCNN)

* [Stick-To/Object-Detection-Tensorflow2](https://github.com/Stick-To/Object-Detection-Tensorflow2)

* [hux999/tf-fcos](https://github.com/hux999/tf-fcos)

### NLP Model 自然语言处理模型

#### Large library 大型库

* [tensorflow/tensor2tensor](https://github.com/tensorflow/tensor2tensor)


Tensor2Tensor或简称T2T，是一个深度学习模型和数据集的库，旨在使深度学习更易于访问并加速ML研究。Google Brain团队和用户社区的研究人员和工程师积极使用和维护T2T 。


* [huggingface/transformers](https://github.com/huggingface/transformers)


TensorFlow 2.0和PyTorch的最新自然语言处理，（以前称为pytorch-transformers和pytorch-pretrained-bert）提供用于自然语言理解（NLU）和自然语言生成（NLG）的最新通用架构（BERT，GPT-2，RoBERTa，XLM，DistilBert，XLNet ...） ）包含超过32种以100多种语言编写的预训练模型，以及TensorFlow 2.0和PyTorch之间的深层互操作性。


#### Model 模型

* [codertimo/BERT-tf2](https://github.com/codertimo/BERT-tf2)

* [ShaneTian/TextCNN](https://github.com/ShaneTian/TextCNN)

* [strutive07/transformer-tensorflow2.0](https://github.com/strutive07/transformer-tensorflow2.0)

* [thisisiron/nmt-attention-tf](https://github.com/thisisiron/nmt-attention-tf)

* [kpe/bert-for-tf2](https://github.com/kpe/bert-for-tf2)

* [akanyaani/gpt-2-tensorflow2.0](https://github.com/akanyaani/gpt-2-tensorflow2.0)

### Point Model 点云模型

* [dgriffiths3/pointnet2-tensorflow2](https://github.com/dgriffiths3/pointnet2-tensorflow2)

## Projects 💛💛💛💛💛

### 视觉较大项目

* [giovgiac/neptune](https://github.com/giovgiac/neptune)

### 分割

* [Shathe/Semantic-Segmentation-Tensorflow-2](https://github.com/Shathe/Semantic-Segmentation-Tensorflow-2)

* [1044197988/Semantic-segmentation-of-remote-sensing-images](https://github.com/1044197988/Semantic-segmentation-of-remote-sensing-images)

### NLP

* [jason9693/MusicTransformer-tensorflow2.0](https://github.com/jason9693/MusicTransformer-tensorflow2.0)

* [xingchensong/Speech-Transformer-tf2.0](https://github.com/xingchensong/Speech-Transformer-tf2.0)

* [drukka/command-words-recognition-keras](https://github.com/drukka/command-words-recognition-keras)

* [bryanlimy/tf2-transformer-chatbot](https://github.com/bryanlimy/tf2-transformer-chatbot)

* [gibrano/chatbot](https://github.com/gibrano/chatbot)

### 目标检测

* [liushuan/YOLO-V3-Tensorflow2.0-Face-Detect-via-Wider-Face](https://github.com/liushuan/YOLO-V3-Tensorflow2.0-Face-Detect-via-Wider-Face)

* [burnpiro/tiny-face-detection-tensorflow2](https://github.com/burnpiro/tiny-face-detection-tensorflow2)

### 验证码识别

* [ybsdegit/captcha_keras](https://github.com/ybsdegit/captcha_keras)

### 文字检测、识别

* [RaidasGrisk/tf2-fots](https://github.com/RaidasGrisk/tf2-fots)

* [RaidasGrisk/tf2-crnn](https://github.com/RaidasGrisk/tf2-crnn)

### 人脸识别

* [DequanZhu/FaceNet-and-FaceLoss-collections-tensorflow2.0](https://github.com/DequanZhu/FaceNet-and-FaceLoss-collections-tensorflow2.0)

* [Fei-Wang/insightface](https://github.com/Fei-Wang/insightface)

* [610265158/Peppa_Pig_Face_Engine](https://github.com/610265158/Peppa_Pig_Face_Engine)

* [610265158/faceboxes-tensorflow](https://github.com/610265158/faceboxes-tensorflow)

## Other 💛💛💛💛💛

### 脚本

* [hacker-lin/train_test_valid_split](https://github.com/hacker-lin/train_test_valid_split)

* [ravindrabharathi/tf_utils](https://github.com/ravindrabharathi/tf_utils)

### 解释性工具

* [sicara/tf-explain](https://github.com/sicara/tf-explain)

### 推荐系统和CTR预测模型

* [JianzhouZhan/Awesome-RecSystem-Models](https://github.com/JianzhouZhan/Awesome-RecSystem-Models)

* [cheungdaven/DeepRec](https://github.com/cheungdaven/DeepRec)

* [Hourout/CTR-keras](https://github.com/Hourout/CTR-keras)

* [hojinYang/recsys-implementation.tensorflow2](https://github.com/hojinYang/recsys-implementation.tensorflow2)

* [SSSxCCC/Recommender-System](https://github.com/SSSxCCC/Recommender-System)

### 用于分布式培训，评估，模型选择和快速原型制作

* [zurutech/ashpy](https://github.com/zurutech/ashpy)

### AutoML

* [keras-team/keras-tuner](https://github.com/keras-team/keras-tuner)

* [keras-team/autokeras](https://github.com/keras-team/autokeras)

* [tensorflow/adanet](https://github.com/tensorflow/adanet)

### 元学习

* [siavash-khodadadeh/MetaLearning-TF2.0](https://github.com/siavash-khodadadeh/MetaLearning-TF2.0)

### 强化学习

* [danaugrs/huskarl](https://github.com/danaugrs/huskarl)

* [keiohta/tf2rl](https://github.com/keiohta/tf2rl)

### 神经结构化学习

* [neural-structured-learning](https://github.com/tensorflow/neural-structured-learning)

### 强大的扩展

* [tensorflow/addons](https://github.com/tensorflow/addons)

### 迭代矩阵平方根归一化网络（称为快速MPN-COV），该网络非常有效，适合大规模数据集

* [XuChunqiao/Tensorflow-Fast-MPNCOV](https://github.com/XuChunqiao/Tensorflow-Fast-MPNCOV)

### TF-GAN是用于培训和评估生成对抗网络GAN的轻量级库

* [tensorflow/gan](https://github.com/tensorflow/gan)

### 官方数据集包

* [tensorflow/datasets](https://github.com/tensorflow/datasets)

### NLP

* [tensorflow/text](https://github.com/tensorflow/text)

### 大型基于TF2的封装扩展库

* [tensorlayer/tensorlayer](https://github.com/tensorlayer/tensorlayer)

### TF2项目模板

* [michaeltinsley/TF2-Project-Template](https://github.com/michaeltinsley/TF2-Project-Template)

### GCN图神经网络

* [breadbread1984/GCN-tf2.0](https://github.com/breadbread1984/GCN-tf2.0)

### RBF径向基

* [PetraVidnerova/rbf_for_tf2](https://github.com/PetraVidnerova/rbf_for_tf2)

### 新型的高性能可解释的深表格式数据学习网络TabNet

* [titu1994/tf-TabNet](https://github.com/titu1994/tf-TabNet)

### 优化器

* [OverLordGoldDragon/keras-adamw](https://github.com/OverLordGoldDragon/keras-adamw)

### 用于单通道语音分离的双路径RNN

* [sp-uhh/dual-path-rnn](https://github.com/sp-uhh/dual-path-rnn)

### 新型数据处理技术

* [AakashKumarNain/AugMix_TF2](https://github.com/AakashKumarNain/AugMix_TF2)

### 使用tensorflow 2构建的多任务学习包

* [AmazaspShumik/mtlearn](https://github.com/AmazaspShumik/mtlearn)

### TensowFlow2.0上的CBAM（卷积块注意模块）实现

* [zhangkaifang/CBAM-TensorFlow2.0](https://github.com/zhangkaifang/CBAM-TensorFlow2.0)

### Spektral是一个基于Keras API和TensorFlow 2的用于图深度学习的Python库。该项目的主要目标是提供一个简单而灵活的框架来创建图神经网络（GNN）。

* [danielegrattarola/spektral](https://github.com/danielegrattarola/spektral)