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 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
    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).



    help wanted:paper implementation

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


    Apache License 2.0

    Citing TensorFlow Model Garden

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

      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{}},
      year = {2020}


    🚀 Github 镜像仓库 🚀


    发行版本 18

    TensorFlow Official Models 2.7.0


    贡献者 152



    • Python 91.1 %
    • Jupyter Notebook 6.4 %
    • C++ 1.7 %
    • Shell 0.4 %
    • Starlark 0.4 %