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Unverified Commit 9c1429cf authored by Jaeyoun Kim's avatar Jaeyoun Kim Committed by GitHub
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Add new templates and update README files (#8467) 

* Update README.md

Add a new section (Announcements)

* Update README.md

Remove description columns from tables
Remove guidelines sections

* Update CONTRIBUTING.md

Add a link to the wiki for guidelines

* Create README_TEMPLATE.md

Add a README template file for a new model

* Create pull_request_template.md

Add a new template for a pull request

* Update README.md

Add a table of contents
Add a new section for archived models and implementations

* Update README.md

Revise "Contributions"

* Update README.md

Update the link to the contribution guidelines

* Update README.md

Update the link to the contribution guidelines.

* Update README.md

Update the link to the contribution guidelines.

* Update README.md

Update the link to the contribution guidelines.
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.github/PULL_REQUEST_TEMPLATE/pull_request_template.md 0 → 100644
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# Description
> :memo: Please include a summary of the change.
>
> * Please also include relevant motivation and context.
> * List any dependencies that are required for this change.
## Type of change
Please delete options that are not relevant.
- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] Documentation update
- [ ] TensorFlow 2 migration
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
- [ ] A new research paper code implementation
## Tests
> :memo: Please describe the tests that you ran to verify your changes.
>
> * Provide instructions so we can reproduce.
> * Please also list any relevant details for your test configuration.
**Test Configuration**:
## Checklist
- [ ] I have signed the [Contributor License Agreement](../../wiki/Contributor-License-Agreements).
- [ ] I have read [guidelines for pull request](../../wiki/Submitting-a-pull-request).
- [ ] My code follows the [coding guidelines](../../wiki/Coding-Guidelines).
- [ ] I have performed a self [code review](../../wiki/Code-review) of my own code.
- [ ] I have commented my code, particularly in hard-to-understand areas.
- [ ] I have made corresponding changes to the documentation.
- [ ] My changes generate no new warnings.
- [ ] I have added tests that prove my fix is effective or that my feature works.
.github/README_TEMPLATE.md 0 → 100644
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> :memo: A README.md template for releasing a paper code implementation to a GitHub repository.
>
> * Template version: 1.0.2020.125
> * 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)
[![Paper](http://img.shields.io/badge/paper-arXiv.YYMM.NNNNN-B3181B.svg)](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.
## Maintainers
> :memo: Provide maintainer information.
* Last name, First name ([@GitHub username](https://github.com/username))
* Last name, First 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
[![tensorFlow 2.1+](https://img.shields.io/badge/TensorFlow-2.1-brightgreen)](https://github.com/tensorflow/tensorflow/releases/tag/v2.1.0)
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](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
> :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
> :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.
CONTRIBUTING.md
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# Contributing guidelines
# How to contribute
If you have created a model and would like to publish it here, please send us a
pull request. For those just getting started with pull requests, GitHub has a
[howto](https://help.github.com/articles/using-pull-requests/).
![Contributors](https://img.shields.io/github/contributors/tensorflow/models)
The code for any model in this repository is licensed under the Apache License
2.0.
We encourage you to contribute to the TensorFlow Model Garden.
In order to accept our code, we have to make sure that we can publish your code:
You have to sign a Contributor License Agreement (CLA).
Please read our [guidelines](../../wiki/How-to-contribute) for details.
***NOTE***: Only [code owners](./CODEOWNERS) are allowed to merge a pull request.
**NOTE**: Only [code owners](./CODEOWNERS) are allowed to merge a pull request.
Please contact the code owners of each model to merge your pull request.
### Contributor License Agreements
Please fill out either the individual or corporate Contributor License Agreement (CLA).
* If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](http://code.google.com/legal/individual-cla-v1.0.html).
* If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](http://code.google.com/legal/corporate-cla-v1.0.html).
Follow either of the two links above to access the appropriate CLA and instructions for how to sign and return it. Once we receive it, we'll be able to accept your pull requests.
***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the repository.
README.md
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......@@ -4,17 +4,19 @@
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.
## Structure
| Folder | Description |
| Directory | Description |
|-----------|-------------|
| [official](official) | • **A collection of example implementations for SOTA models using the latest TensorFlow 2's high-level APIs**<br />• Officially maintained, supported, and kept up to date with the latest TensorFlow 2 APIs<br />• Reasonably optimized for fast performance while still being easy to read |
| [research](research) | • A collection of research model implementations in TensorFlow 1 or 2 by researchers<br />• Up to the individual researchers to maintain the model implementations and/or provide support on issues and pull requests |
| [official](official) | • A collection of example implementations for SOTA models using the latest TensorFlow 2's high-level APIs<br />• Officially maintained, supported, and kept up to date with the latest TensorFlow 2 APIs by TensorFlow<br />• Reasonably optimized for fast performance while still being easy to read |
| [research](research) | • A collection of research model implementations in TensorFlow 1 or 2 by researchers<br />• Maintained and supported by researchers |
| [community](community) | • A curated list of the GitHub repositories with machine learning models and implementations powered by TensorFlow 2 |
## Contribution guidelines
## [Announcements](../../wiki/Announcements)
* March 31, 2020: [Introducing the Model Garden for TensorFlow 2](https://blog.tensorflow.org/2020/03/introducing-model-garden-for-tensorflow-2.html) ([Tweet](https://twitter.com/TensorFlow/status/1245029834633297921))
## Contributions
If you want to contribute to models, please review the [contribution guidelines](CONTRIBUTING.md).
If you want to contribute, please review the [contribution guidelines](../../wiki/How-to-contribute).
## License
......
community/README.md
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This repository provides a curated list of the GitHub repositories with machine learning models and implementations powered by TensorFlow 2.
**Disclosure**: Contributing companies or individuals are responsible for maintaining their repositories.
**Note**: Contributing companies or individuals are responsible for maintaining their repositories.
## Models / Implementations
......@@ -16,6 +16,6 @@ This repository provides a curated list of the GitHub repositories with machine
| [Mask R-CNN](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/MaskRCNN) | [Mask R-CNN](https://arxiv.org/abs/1703.06870) | • Automatic Mixed Precision<br/>• Multi-GPU training support with Horovod<br/>• TensorRT | [NVIDIA](https://github.com/NVIDIA) |
| [U-Net Medical Image Segmentation](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/UNet_Medical) | [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597) | • Automatic Mixed Precision<br/>• Multi-GPU training support with Horovod<br/>• TensorRT | [NVIDIA](https://github.com/NVIDIA) |
## Contributions
If you have a suggestion for the community model repository, please submit a pull request.
If you want to contribute, please review the [contribution guidelines](../../../wiki/How-to-contribute).
official/README.md
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......@@ -6,41 +6,68 @@ The TensorFlow official models are a collection of models
that use TensorFlow’s high-level APIs.
They are intended to be well-maintained, tested, and kept up to date
with the latest TensorFlow API.
They should also be reasonably optimized for fast performance while still
being easy to read.
These models are used as end-to-end tests, ensuring that the models run
with the same or improved speed and performance with each new TensorFlow build.
## Model Implementations
## More models to come!
### Natural Language Processing
The team is actively developing new models.
In the near future, we will add:
* State-of-the-art language understanding models:
More members in Transformer family
* Start-of-the-art image classification models:
EfficientNet, MnasNet, and variants
* A set of excellent objection detection models.
## Table of Contents
| Model | Description | Reference |
| ----- | ----------- | --------- |
| [ALBERT](nlp/albert) | A Lite BERT for Self-supervised Learning of Language Representations | [arXiv:1909.11942](https://arxiv.org/abs/1909.11942) |
| [BERT](nlp/bert) | A powerful pre-trained language representation model: BERT (Bidirectional Encoder Representations from Transformers) | [arXiv:1810.04805](https://arxiv.org/abs/1810.04805) |
| [NHNet](nlp/nhnet) | A transformer-based multi-sequence to sequence model: Generating Representative Headlines for News Stories | [arXiv:2001.09386](https://arxiv.org/abs/2001.09386) |
| [Transformer](nlp/transformer) | A transformer model to translate the WMT English to German dataset | [arXiv:1706.03762](https://arxiv.org/abs/1706.03762) |
| [XLNet](nlp/xlnet) | XLNet: Generalized Autoregressive Pretraining for Language Understanding | [arXiv:1906.08237](https://arxiv.org/abs/1906.08237) |
- [Models and Implementations](#models-and-implementations)
* [Computer Vision](#computer-vision)
+ [Image Classification](#image-classification)
+ [Object Detection and Segmentation](#object-detection-and-segmentation)
* [Natural Language Processing](#natural-language-processing)
* [Recommendation](#recommendation)
- [How to get started with the official models](#how-to-get-started-with-the-official-models)
## Models and Implementations
### Computer Vision
| Model | Description | Reference |
| ----- | ----------- | --------- |
| [MNIST](vision/image_classification) | A basic model to classify digits from the MNIST dataset | [Link](http://yann.lecun.com/exdb/mnist/) |
| [ResNet](vision/image_classification) | A deep residual network for image recognition | [arXiv:1512.03385](https://arxiv.org/abs/1512.03385) |
| [RetinaNet](vision/detection) | A fast and powerful object detector | [arXiv:1708.02002](https://arxiv.org/abs/1708.02002) |
| [Mask R-CNN](vision/detection) | An object detection and instance segmentation model | [arXiv:1703.06870](https://arxiv.org/abs/1703.06870) |
#### Image Classification
| Model | Reference (Paper) |
|-------|-------------------|
| [MNIST](vision/image_classification) | A basic model to classify digits from the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) |
| [ResNet](vision/image_classification) | [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) |
### Other models
#### Object Detection and Segmentation
| Model | Description | Reference |
| ----- | ----------- | --------- |
| [NCF](recommendation) | Neural Collaborative Filtering model for recommendation tasks | [arXiv:1708.05031](https://arxiv.org/abs/1708.05031) |
| Model | Reference (Paper) |
|-------|-------------------|
| [RetinaNet](vision/detection) | [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) |
| [Mask R-CNN](vision/detection) | [Mask R-CNN](https://arxiv.org/abs/1703.06870) |
### Natural Language Processing
---
| Model | Reference (Paper) |
|-------|-------------------|
| [ALBERT (A Lite BERT)](nlp/albert) | [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) |
| [BERT (Bidirectional Encoder Representations from Transformers)](nlp/bert) | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) |
| [NHNet (News Headline generation model)](nlp/nhnet) | [Generating Representative Headlines for News Stories](https://arxiv.org/abs/2001.09386) |
| [Transformer](nlp/transformer) | [Attention Is All You Need](https://arxiv.org/abs/1706.03762) |
| [XLNet](nlp/xlnet) | [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) |
## How to get started with the Model Garden official models
### Recommendation
| Model | Reference (Paper) |
|-------|-------------------|
| [NCF](recommendation) | [Neural Collaborative Filtering](https://arxiv.org/abs/1708.05031) |
## How to get started with the official models
* The models in the master branch are developed using TensorFlow 2,
and they target the TensorFlow [nightly binaries](https://github.com/tensorflow/tensorflow#installation)
......@@ -108,44 +135,6 @@ os.environ['PYTHONPATH'] += ":/path/to/models"
pip3 install --user -r official/requirements.txt
```
---
## More models to come!
The team is actively developing new models.
In the near future, we will add:
- State-of-the-art language understanding models:
More members in Transformer family
- Start-of-the-art image classification models:
EfficientNet, MnasNet and variants.
- A set of excellent objection detection models.
If you would like to make any fixes or improvements to the models, please
[submit a pull request](https://github.com/tensorflow/models/compare).
---
## Contributions
Every model should follow our guidelines to uphold our objectives of readable,
usable, and maintainable code.
### General Guidelines
- Code should be well documented and tested.
- Runnable from a blank environment with ease.
- Trainable on: single GPU/CPU (baseline), multiple GPUs & TPUs
- Compatible with Python 3 (using [six](https://pythonhosted.org/six/)
when being compatible with Python 2 is necessary)
- Conform to
[Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)
### Implementation Guidelines
These guidelines are to ensure consistent model implementations for
better readability and maintainability.
- Use [common utility functions](utils)
- Export SavedModel at the end of the training.
- Consistent flags and flag-parsing library ([read more here](utils/flags/guidelines.md))
If you want to contribute, please review the [contribution guidelines](../../../wiki/How-to-contribute).
research/README.md
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