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)

Searching Database…

77.6K ·GitHub
Models
What is this?TensorFlow Model Garden is a repository of state-of-the-art model implementations and examples built with TensorFlow, covering officially maintained models, research models, and a curated community list.
Is this a single library or a collection of separate projects?It is organized into four directories: 'official' (SOTA models kept up to date with TF2 APIs), 'research' (TF1/TF2 research implementations), 'community' (a curated list of external TF2-powered repos), and 'orbit' (a lightweight custom training-loop library).
Do I need anything extra for NLP models?For NLP packages you should also install 'tensorflow-text-nightly' in addition to the core dependencies that pip installs automatically.
Does the pip package always match the latest code on GitHub?No, the stable 'tf-models-official' package may lag behind the master branch; you need the nightly build to get the latest changes.
Can training run on GPU or TPU, not just CPU?Yes, the included 'orbit' training-loop library integrates with tf.distribute and supports running on CPU, GPU, and TPU devices.
What is Orbit used for?Orbit is a flexible, lightweight library for writing customized training loops in TensorFlow 2.x, designed to work seamlessly with tf.distribute across CPU, GPU, and TPU.
Can I use this on a Colab notebook?Yes, the README notes that on Colab you should set the Python path using os.environ instead of the Windows PowerShell method.
Should I cite anything if I use these models in research?Yes, the README asks that you cite the TensorFlow Model Garden repository if you use it in your research.
What are similar projects?SolarWM, Chinese-BERT-wwm, spaCy, jeeves, spleeter, Index-Translate, facenet, YOLOX
Which topics is it in?model, pretrained-models, tensorflow, neural-network, transformer, nlp, computer-vision, cuda, benchmark, deep-learning, framework, research
Creator
tensorflow
Size
644.9 MB
Stars
77.6K
Updated
CommitOct 07Releasev2.20.0Feb 11
Age
10y
Commits · 90 Days