Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tensorflow/models
Models and examples built with TensorFlow
https://github.com/tensorflow/models
Last synced: 7 days ago
JSON representation
Models and examples built with TensorFlow
- Host: GitHub
- URL: https://github.com/tensorflow/models
- Owner: tensorflow
- License: other
- Created: 2016-02-05T01:15:20.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-08-30T23:53:47.000Z (2 months ago)
- Last Synced: 2024-09-01T10:02:34.168Z (2 months ago)
- Language: Python
- Homepage:
- Size: 607 MB
- Stars: 76,913
- Watchers: 2,730
- Forks: 45,805
- Open Issues: 1,249
-
Metadata Files:
- Readme: .github/README_TEMPLATE.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS
- Security: SECURITY.md
- Authors: AUTHORS
Awesome Lists containing this project
- awesome-machine-learning - tensorflow models
- awesome-starred - models - Models and examples built with TensorFlow (Python)
- awesome-starts - tensorflow/models - Models and examples built with TensorFlow (Python)
- Awesome-Tensorflow2 - tensorflow/models
- awesome-list - TensorFlow Model Garden - Models and examples built with TensorFlow (Deep Learning Framework / High-Level DL APIs)
- Awesome-CoreML-Models - TensorFlow Models - Models for TensorFlow. (Miscellaneous)
- awesome - tensorflow/models - Models and examples built with TensorFlow (Python)
- AiTreasureBox - tensorflow/models - 11-02_77155_0](https://img.shields.io/github/stars/tensorflow/models.svg)|Models and examples built with TensorFlow| (Repos)
README
> :memo: A README.md template for releasing a paper code implementation to a GitHub repository.
>
> * Template version: 1.0.2020.170
> * Please modify sections depending on needs.
# Model name, Paper title, or Project Name
> :memo: Add a badge for the ArXiv identifier of your paper (arXiv:YYMM.NNNNN)
[](https://arxiv.org/abs/...)
This repository is the official or unofficial implementation of the following paper.
* Paper title: [Paper Title](https://arxiv.org/abs/YYMM.NNNNN)
## Description
> :memo: Provide description of the model.
>
> * Provide brief information of the algorithms used.
> * Provide links for demos, blog posts, etc.
## History
> :memo: Provide a changelog.
## Authors or Maintainers
> :memo: Provide maintainer information.
* Full name ([@GitHub username](https://github.com/username))
* Full name ([@GitHub username](https://github.com/username))
## Table of Contents
> :memo: Provide a table of contents to help readers navigate a lengthy README document.
## Requirements
[](https://github.com/tensorflow/tensorflow/releases/tag/v2.1.0)
[](https://www.python.org/downloads/release/python-360/)
> :memo: Provide details of the software required.
>
> * Add a `requirements.txt` file to the root directory for installing the necessary dependencies.
> * Describe how to install requirements using pip.
> * Alternatively, create INSTALL.md.
To install requirements:
```setup
pip install -r requirements.txt
```
## Results
[](https://tfhub.dev/...)
> :memo: Provide a table with results. (e.g., accuracy, latency)
>
> * Provide links to the pre-trained models (checkpoint, SavedModel files).
> * Publish TensorFlow SavedModel files on TensorFlow Hub (tfhub.dev) if possible.
> * Add links to [TensorBoard.dev](https://tensorboard.dev/) for visualizing metrics.
>
> An example table for image classification results
>
> ### Image Classification
>
> | Model name | Download | Top 1 Accuracy | Top 5 Accuracy |
> |------------|----------|----------------|----------------|
> | Model name | [Checkpoint](https://drive.google.com/...), [SavedModel](https://tfhub.dev/...) | xx% | xx% |
## Dataset
> :memo: Provide information of the dataset used.
## Training
> :memo: Provide training information.
>
> * Provide details for preprocessing, hyperparameters, random seeds, and environment.
> * Provide a command line example for training.
Please run this command line for training.
```shell
python3 ...
```
## Evaluation
> :memo: Provide an evaluation script with details of how to reproduce results.
>
> * Describe data preprocessing / postprocessing steps.
> * Provide a command line example for evaluation.
Please run this command line for evaluation.
```shell
python3 ...
```
## References
> :memo: Provide links to references.
## License
[](https://opensource.org/licenses/Apache-2.0)
> :memo: Place your license text in a file named LICENSE in the root of the repository.
>
> * Include information about your license.
> * Reference: [Adding a license to a repository](https://help.github.com/en/github/building-a-strong-community/adding-a-license-to-a-repository)
This project is licensed under the terms of the **Apache License 2.0**.
## Citation
> :memo: Make your repository citable.
>
> * Reference: [Making Your Code Citable](https://guides.github.com/activities/citable-code/)
If you want to cite this repository in your research paper, please use the following information.