models README

The project's README from github.com/tensorflow/models. As one markdown file: README.md.

Python tf-models-official PyPI

Welcome to the Model Garden for TensorFlow

The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users can take full advantage of TensorFlow for their research and product development.

To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models are suitable.

Directory Description
official • A collection of example implementations for SOTA models using the latest TensorFlow 2's high-level APIs
• Officially maintained, supported, and kept up to date with the latest TensorFlow 2 APIs by TensorFlow
• Reasonably optimized for fast performance while still being easy to read
For more details on the capabilities, check the guide on the Model-garden
research • A collection of research model implementations in TensorFlow 1 or 2 by researchers
• Maintained and supported by researchers
community • A curated list of the GitHub repositories with machine learning models and implementations powered by TensorFlow 2
orbit • A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU).

Installation

To install the current release of tensorflow-models, please follow any one of the methods described below.

Method 1: Install the TensorFlow Model Garden pip package

tf-models-official is the stable Model Garden package. Please check out the releases to see what are available modules.

pip3 will install all models and dependencies automatically.

pip3 install tf-models-official

Please check out our examples:

Note that tf-models-official may not include the latest changes in the master branch of this github repo. To include latest changes, you may install tf-models-nightly, which is the nightly Model Garden package created daily automatically.

pip3 install tf-models-nightly

Method 2: Clone the source

  1. Clone the GitHub repository:
git clone https://github.com/tensorflow/models.git
  1. Add the top-level /models folder to the Python path.
export PYTHONPATH=$PYTHONPATH:/path/to/models

If you are using in a Windows environment, you may need to use the following command with PowerShell:

$env:PYTHONPATH += ":\path\to\models"

If you are using a Colab notebook, please set the Python path with os.environ.

import os
os.environ['PYTHONPATH'] += ":/path/to/models"
  1. Install other dependencies
pip3 install --user -r models/official/requirements.txt

Finally, if you are using nlp packages, please also install tensorflow-text-nightly:

pip3 install tensorflow-text-nightly

Announcements

Please check this page for recent announcements.

Contributions

help wanted:paper implementation

If you want to contribute, please review the contribution guidelines.

License

Apache License 2.0

Citing TensorFlow Model Garden

If you use TensorFlow Model Garden in your research, please cite this repository.

@misc{tensorflowmodelgarden2020,
  author = {Hongkun Yu and Chen Chen and Xianzhi Du and Yeqing Li and Abdullah Rashwan and Le Hou and Pengchong Jin and Fan Yang
            and Frederick Liu and Jaeyoun Kim and Jing Li},
  title = {{TensorFlow Model Garden}},
  howpublished = {\url{https://github.com/tensorflow/models}},
  year = {2020}
}

models (tensorflow/models)

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77.6K ·GitHub
这是什么?TensorFlow Model Garden 是一个基于 TensorFlow 构建的代码仓库,收录了最先进的模型实现和示例,包括官方维护的模型、研究模型以及精选的社区项目列表。
这是一个单一的库,还是多个独立项目的集合?它分为四个目录:'official'(使用 TF2 API 保持更新的最先进(SOTA)模型)、'research'(TF1/TF2 的研究实现)、'community'(精选的基于 TF2 的外部仓库列表)以及 'orbit'(一个轻量级的自定义训练循环库)。
使用 NLP 模型还需要额外安装什么吗?对于 NLP 相关的包,除了 pip 会自动安装的核心依赖外,还应安装 'tensorflow-text-nightly'。
pip 安装的包是否总是与 GitHub 上的最新代码一致?不一致。稳定版 'tf-models-official' 包可能落后于 master 分支;如需获取最新改动,需要使用每日构建版(nightly)。
训练能否在 GPU 或 TPU 上运行,而不仅限于 CPU?可以。所包含的 'orbit' 训练循环库与 tf.distribute 集成,支持在 CPU、GPU 和 TPU 设备上运行。
Orbit 有什么用途?Orbit 是一个灵活、轻量级的库,用于在 TensorFlow 2.x 中编写自定义训练循环,设计上可与 tf.distribute 无缝配合,支持 CPU、GPU 和 TPU。
我能在 Colab 笔记本中使用它吗?可以。README 中提到,在 Colab 上应使用 os.environ 设置 Python 路径,而不是 Windows PowerShell 的方法。
如果在研究中使用这些模型,是否需要引用什么?需要。README 要求,如果在研究中使用了 TensorFlow Model Garden,请引用该仓库。
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