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tensorflow models
Models and examples built with TensorFlow
Stars
77,660
Forks
44,859
Watchers: 77,660
Language
License: Other
Repository Radar Score
66 / 100
Growth
- 7d
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Score breakdown
- popularity 94
- growth 0
- activity 100
- freshness 100
- community 80
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Our team builds with modern open-source stacks. Tell us what you are shipping.
Get a quote →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). |
To install the current release of tensorflow-models, please follow any one of the methods described below.
Details
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-officialPlease check out our examples:
- basic library import
- nlp model building to learn how to use a PIP package.
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-nightlyDetails
- Clone the GitHub repository:
git clone https://github.com/tensorflow/models.git- Add the top-level /models folder to the Python path.
export PYTHONPATH=$PYTHONPATH:/path/to/modelsIf 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"- Install other dependencies
pip3 install --user -r models/official/requirements.txtFinally, if you are using nlp packages, please also install tensorflow-text-nightly:
pip3 install tensorflow-text-nightlyPlease check this page for recent announcements.
If you want to contribute, please review the contribution guidelines.
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}
}
Repository Radar analysis
Deterministic insights derived from public metadata and our observations — not personal testing or reviews.
Why this repository is interesting
- High absolute popularity (77,660 stars) signals broad adoption.
- Maintained recently (last push 2 weeks ago).
Who should use it
- Developers working primarily with Python
Potential use cases
- Reference or evaluate Python open-source approaches in this domain
Strengths
- Recent repository activity
- README present in our index
- Declared license: Other
- Substantial fork count (44,859) suggests reuse and contribution interest
Limitations / considerations
- Insights are derived from public metadata and our observations — not a substitute for code review
What to watch
- Re-check last push, issues, and releases on GitHub before production adoption
Strong signals: Strong community interest · Active maintenance
Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.