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        "description": "![Tensorpack](https://github.com/tensorpack/tensorpack/raw/master/.github/tensorpack.png)\n\nTensorpack is a neural network training interface based on TensorFlow.\n\n[![Build Status](https://travis-ci.org/tensorpack/tensorpack.svg?branch=master)](https://travis-ci.org/tensorpack/tensorpack)\n[![ReadTheDoc](https://readthedocs.org/projects/tensorpack/badge/?version=latest)](http://tensorpack.readthedocs.io)\n[![Gitter chat](https://img.shields.io/badge/chat-on%20gitter-46bc99.svg)](https://gitter.im/tensorpack/users)\n[![model-zoo](https://img.shields.io/badge/model-zoo-brightgreen.svg)](http://models.tensorpack.com)\n## Features:\n\nIt's Yet Another TF high-level API, with __speed__, and __flexibility__ built together.\n\n1. Focus on __training speed__.\n\t+ Speed comes for free with Tensorpack -- it uses TensorFlow in the __efficient way__ with no extra overhead.\n\t  On common CNNs, it runs training [1.2~5x faster](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers) than the equivalent Keras code.\n\t\tYour training can probably gets faster if written with Tensorpack.\n\n\t+ Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use.\n    It scales as well as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks).\n\n\t+ See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for\n    some benchmark scripts.\n\n2. Focus on __large datasets__.\n\t+ [You don't usually need `tf.data`](https://tensorpack.readthedocs.io/tutorial/philosophy/dataflow.html#alternative-data-loading-solutions).\n    Symbolic programming often makes data processing harder.\n\t  Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization.\n\n3. It's not a model wrapper.\n\t+ There are too many symbolic function wrappers in the world. Tensorpack includes only a few common models.\n\t  But you can use any symbolic function library inside Tensorpack, including tf.layers/Keras/slim/tflearn/tensorlayer/....\n\nSee [tutorials and documentations](http://tensorpack.readthedocs.io/tutorial/index.html#user-tutorials) to know more about these features.\n\n## Examples:\n\nWe refuse toy examples.\nInstead of showing tiny CNNs trained on MNIST/Cifar10,\nwe provide training scripts that reproduce well-known papers.\n\nWe refuse low-quality implementations.\nUnlike most open source repos which only __implement__ papers,\n[Tensorpack examples](examples) faithfully __reproduce__ papers,\ndemonstrating its __flexibility__ for actual research.\n\n### Vision:\n+ [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet.\n+ [Train Mask/Faster R-CNN on COCO object detection](examples/FasterRCNN)\n+ [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN.\n+ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net)\n+ [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED)\n+ [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer)\n+ [Visualize CNN saliency maps](examples/Saliency)\n+ [Similarity learning on MNIST](examples/SimilarityLearning)\n\n### Reinforcement Learning:\n+ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.\n+ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym)\n\n### Speech / NLP:\n+ [LSTM-CTC for speech recognition](examples/CTC-TIMIT)\n+ [char-rnn for fun](examples/Char-RNN)\n+ [LSTM language model on PennTreebank](examples/PennTreebank)\n\n## Install:\n\nDependencies:\n\n+ Python 2.7 or 3.3+. Python 2.7 is supported until [it retires in 2020](https://pythonclock.org/).\n+ Python bindings for OpenCV. (Optional, but required by a lot of features)\n+ TensorFlow \u2265 1.3, < 2. (Not required if you only want to use `tensorpack.dataflow` alone as a data processing library)\n```\npip install --upgrade git+https://github.com/tensorpack/tensorpack.git\n# or add `--user` to install to user's local directories\n```\n\nPlease note that tensorpack is not yet stable.\nIf you use tensorpack in your code, remember to mark the exact version of tensorpack you use as your dependencies.\n\n## Citing Tensorpack:\n\nIf you use Tensorpack in your research or wish to refer to the examples, please cite with:\n```\n@misc{wu2016tensorpack,\n  title={Tensorpack},\n  author={Wu, Yuxin and others},\n  howpublished={\\url{https://github.com/tensorpack/}},\n  year={2016}\n}\n```\n\n\n",
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