model code

README.md

-# tf_model
-

models-2.13.1/.github/ISSUE_TEMPLATE/00-official-bug-report-issue.md

+---
+name: "[Official Model] Bug Report"
+about: Use this template for reporting a bug for the “official” directory
+labels: type:bug,models:official
+
+---
+
+# Prerequisites
+
+Please answer the following questions for yourself before submitting an issue.
+
+- [ ] I am using the latest TensorFlow Model Garden release and TensorFlow 2.
+- [ ] I am reporting the issue to the correct repository. (Model Garden official or research directory)
+- [ ] I checked to make sure that this issue has not been filed already.
+
+## 1. The entire URL of the file you are using
+
+https://github.com/tensorflow/models/tree/master/official/...
+
+## 2. Describe the bug
+
+A clear and concise description of what the bug is.
+
+## 3. Steps to reproduce
+
+Steps to reproduce the behavior.
+
+## 4. Expected behavior
+
+A clear and concise description of what you expected to happen.
+
+## 5. Additional context
+
+Include any logs that would be helpful to diagnose the problem.
+
+## 6. System information
+
+- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
+- Mobile device name if the issue happens on a mobile device:
+- TensorFlow installed from (source or binary):
+- TensorFlow version (use command below):
+- Python version:
+- Bazel version (if compiling from source):
+- GCC/Compiler version (if compiling from source):
+- CUDA/cuDNN version:
+- GPU model and memory:
+
+<!-- 
+Collect system information using our environment capture script.
+https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh
+
+You can also obtain the TensorFlow version with:
+
+1. TensorFlow 1.0
+`python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"`
+
+2. TensorFlow 2.0
+`python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"`
+-->

models-2.13.1/.github/ISSUE_TEMPLATE/10-official-documentation-issue.md

+---
+name: "[Official Model] Documentation Issue"
+about: Use this template for reporting a documentation issue for the “official” directory
+labels: type:docs,models:official
+
+---
+
+# Prerequisites
+
+Please answer the following question for yourself before submitting an issue.
+
+- [ ] I checked to make sure that this issue has not been filed already.
+
+## 1. The entire URL of the documentation with the issue
+
+https://github.com/tensorflow/models/tree/master/official/...
+
+## 2. Describe the issue
+
+A clear and concise description of what needs to be changed.

models-2.13.1/.github/ISSUE_TEMPLATE/20-official-feature-request-issue.md

+---
+name: "[Official Model] Feature request"
+about: Use this template for raising a feature request for the “official” directory
+labels: type:feature,models:official
+
+---
+
+# Prerequisites
+
+Please answer the following question for yourself before submitting an issue.
+
+- [ ] I checked to make sure that this feature has not been requested already.
+
+## 1. The entire URL of the file you are using
+
+https://github.com/tensorflow/models/tree/master/official/...
+
+## 2. Describe the feature you request
+
+A clear and concise description of what you want to happen.
+
+## 3. Additional context
+
+Add any other context about the feature request here.
+
+## 4. Are you willing to contribute it? (Yes or No)

models-2.13.1/.github/ISSUE_TEMPLATE/30-research-bug-report-issue.md

+---
+name: "[Research Model] Bug Report"
+about: Use this template for reporting a bug for the “research” directory
+labels: type:bug,models:research
+
+---
+# Prerequisites
+
+Please answer the following questions for yourself before submitting an issue.
+
+- [ ] I am using the latest TensorFlow Model Garden release and TensorFlow 2.
+- [ ] I am reporting the issue to the correct repository. (Model Garden official or research directory)
+- [ ] I checked to make sure that this issue has not already been filed.
+
+## 1. The entire URL of the file you are using
+
+https://github.com/tensorflow/models/tree/master/research/...
+
+## 2. Describe the bug
+
+A clear and concise description of what the bug is.
+
+## 3. Steps to reproduce
+
+Steps to reproduce the behavior.
+
+## 4. Expected behavior
+
+A clear and concise description of what you expected to happen.
+
+## 5. Additional context
+
+Include any logs that would be helpful to diagnose the problem.
+
+## 6. System information
+
+- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
+- Mobile device name if the issue happens on a mobile device:
+- TensorFlow installed from (source or binary):
+- TensorFlow version (use command below):
+- Python version:
+- Bazel version (if compiling from source):
+- GCC/Compiler version (if compiling from source):
+- CUDA/cuDNN version:
+- GPU model and memory:
+
+<!-- 
+Collect system information using our environment capture script.
+https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh
+
+You can also obtain the TensorFlow version with:
+
+1. TensorFlow 1.0
+`python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"`
+
+2. TensorFlow 2.0
+`python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"`
+-->

models-2.13.1/.github/ISSUE_TEMPLATE/40-research-documentation-issue.md

+---
+name: "[Research Model] Documentation Issue"
+about: Use this template for reporting a documentation issue for the “research” directory
+labels: type:docs,models:research
+
+---
+
+# Prerequisites
+
+Please answer the following question for yourself before submitting an issue.
+
+- [ ] I checked to make sure that this issue has not been filed already.
+
+## 1. The entire URL of the documentation with the issue
+
+https://github.com/tensorflow/models/tree/master/research/...
+
+## 2. Describe the issue
+
+A clear and concise description of what needs to be changed.

models-2.13.1/.github/ISSUE_TEMPLATE/50-research-feature-request-issue.md

+---
+name: "[Research Model] Feature Request"
+about: Use this template for raising a feature request for the “research” directory
+labels: type:feature,models:research
+
+---
+
+# Prerequisites
+
+Please answer the following question for yourself before submitting an issue.
+
+- [ ] I checked to make sure that this feature has not been requested already.
+
+## 1. The entire URL of the file you are using
+
+https://github.com/tensorflow/models/tree/master/research/...
+
+## 2. Describe the feature you request
+
+A clear and concise description of what you want to happen.
+
+## 3. Additional context
+
+Add any other context about the feature request here.
+
+## 4. Are you willing to contribute it? (Yes or No)

models-2.13.1/.github/ISSUE_TEMPLATE/60-questions-help-issue.md

+---
+name: Questions and Help
+about: Use this template for Questions and Help.
+labels: type:support
+
+---
+<!--
+As per our GitHub Policy (https://github.com/tensorflow/models/blob/master/ISSUES.md), we only address code bugs, documentation issues, and feature requests on GitHub.
+
+We will automatically close questions and help related issues.
+
+Please go to Stack Overflow (http://stackoverflow.com/questions/tagged/tensorflow-model-garden) for questions and help.
+
+-->

models-2.13.1/.github/ISSUE_TEMPLATE/config.yml

+blank_issues_enabled: false

models-2.13.1/.github/PULL_REQUEST_TEMPLATE.md

+# 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
+
+For a new feature or function, please create an issue first to discuss it
+with us before submitting a pull request.
+
+Note: 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
+- [ ] Other (Specify)
+
+## 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](https://github.com/tensorflow/models/wiki/Contributor-License-Agreements).
+- [ ] I have read [guidelines for pull request](https://github.com/tensorflow/models/wiki/Submitting-a-pull-request).
+- [ ] My code follows the [coding guidelines](https://github.com/tensorflow/models/wiki/Coding-guidelines).
+- [ ] I have performed a self [code review](https://github.com/tensorflow/models/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.

models-2.13.1/.github/README_TEMPLATE.md

+> :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)
+
+[![Paper](http://img.shields.io/badge/Paper-arXiv.YYMM.NNNNN-B3181B?logo=arXiv)](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
+
+[![TensorFlow 2.1](https://img.shields.io/badge/TensorFlow-2.1-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v2.1.0)
+[![Python 3.6](https://img.shields.io/badge/Python-3.6-3776AB)](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
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](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.

models-2.13.1/.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# mypy
+.mypy_cache
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+
+# PyCharm
+.idea/
+
+# For mac
+.DS_Store

models-2.13.1/AUTHORS

+# This is the official list of authors for copyright purposes.
+# This file is distinct from the CONTRIBUTORS files.
+# See the latter for an explanation.
+
+# Names should be added to this file as:
+# Name or Organization <email address>
+# The email address is not required for organizations.
+
+Google Inc.
+David Dao <daviddao@broad.mit.edu>

models-2.13.1/CODEOWNERS

+* @tensorflow/tf-garden-team @tensorflow/tf-model-garden-team
+/official/ @rachellj218 @saberkun @jaeyounkim
+/official/nlp/ @saberkun @chenGitHuber @lehougoogle @rachellj218
+/official/vision/ @pengchongjin @xianzhidu @yeqingli @arashwan @saberkun @rachellj218
+/research/adv_imagenet_models/ @alexeykurakin
+/research/adversarial_crypto/ @dave-andersen
+/research/adversarial_logit_pairing/ @alexeykurakin
+/research/adversarial_text/ @rsepassi @a-dai
+/research/attention_ocr/ @xavigibert
+/research/audioset/ @plakal @dpwe
+/research/autoaugment/* @barretzoph
+/research/autoencoders/ @snurkabill
+/research/brain_coder/ @danabo
+/research/cognitive_mapping_and_planning/ @s-gupta
+/research/compression/ @nmjohn
+/research/cvt_text/ @clarkkev @lmthang
+/research/deep_contextual_bandits/ @rikel
+/research/deep_speech/ @yhliang2018
+/research/deeplab/ @aquariusjay @yknzhu @gpapan
+/research/delf/ @andrefaraujo
+/research/domain_adaptation/ @bousmalis @dmrd
+/research/efficient-hrl/ @ofirnachum
+/research/feelvos/ @pvoigtlaender @yuningchai @aquariusjay
+/research/fivo/ @dieterichlawson
+/research/global_objectives/ @mackeya-google
+/research/im2txt/ @cshallue
+/research/inception/ @shlens @vincentvanhoucke
+/research/keypointnet/ @mnorouzi
+/research/learned_optimizer/ @olganw @nirum
+/research/learning_to_remember_rare_events/ @lukaszkaiser @ofirnachum
+/research/learning_unsupervised_learning/ @lukemetz @nirum
+/research/lexnet_nc/ @vered1986 @waterson
+/research/lfads/ @jazcollins @sussillo
+/research/lm_1b/ @oriolvinyals @panyx0718
+/research/lm_commonsense/ @thtrieu
+/research/lstm_object_detection/ @yinxiaoli @yongzhe2160
+/research/marco/ @vincentvanhoucke
+/research/maskgan/ @liamb315 @a-dai
+/research/namignizer/ @knathanieltucker
+/research/neural_gpu/ @lukaszkaiser
+/research/neural_programmer/ @arvind2505
+/research/next_frame_prediction/ @panyx0718
+/research/object_detection/ @jch1 @tombstone @pkulzc
+/research/pcl_rl/ @ofirnachum
+/research/ptn/ @xcyan @arkanath @hellojas @honglaklee
+/research/qa_kg/ @yuyuz
+/research/real_nvp/ @laurent-dinh
+/research/rebar/ @gjtucker
+/research/sentiment_analysis/ @sculd
+/research/seq2species/ @apbusia @depristo
+/research/skip_thoughts/ @cshallue
+/research/slim/ @sguada @marksandler2
+/research/steve/ @buckman-google
+/research/street/ @theraysmith
+/research/struct2depth/ @aneliaangelova
+/research/swivel/ @waterson
+/research/tcn/ @coreylynch @sermanet
+/research/textsum/ @panyx0718 @peterjliu
+/research/transformer/ @daviddao
+/research/vid2depth/ @rezama
+/research/video_prediction/ @cbfinn

models-2.13.1/CONTRIBUTING.md

+# How to contribute
+
+![Contributors](https://img.shields.io/github/contributors/tensorflow/models)
+
+We encourage you to contribute to the TensorFlow Model Garden.
+
+Please read our [guidelines](../../wiki/How-to-contribute) for details.
+
+**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.

models-2.13.1/ISSUES.md

+# If you open a GitHub issue, here is our policy.
+
+* It must be a **bug**, a **feature request**, or a significant problem
+with **documentation**.
+  * Please send a pull request instead for small documentation fixes.
+* The required form must be filled out.
+* The issue should be related to the repository it is created in.
+
+General help and support should be sought on [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow-model-garden) or other non-GitHub channels.
+
+[![](https://img.shields.io/stackexchange/stackoverflow/t/tensorflow-model-garden)](https://stackoverflow.com/questions/tagged/tensorflow-model-garden)
+
+TensorFlow developers respond to issues.
+We want to focus on work that benefits the whole community such as fixing bugs
+and adding new features.
+It helps us to address bugs and feature requests in a timely manner.
+
+--- 
+
+Please understand that research models in the [research directory](https://github.com/tensorflow/models/tree/master/research)
+included in this repository are experimental and research-style code.
+They are not officially supported by the TensorFlow team.
+
+

models-2.13.1/LICENSE

+Copyright 2016 The TensorFlow Authors.  All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
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models-2.13.1/README.md

+![Logo](https://storage.googleapis.com/model_garden_artifacts/TF_Model_Garden.png)
+
+# 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.
+
+| 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 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 |
+
+## [Announcements](https://github.com/tensorflow/models/wiki/Announcements)
+
+| Date | News |
+|------|------|
+| June 17, 2020 | [Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection](https://github.com/tensorflow/models/tree/master/research/object_detection#june-17th-2020) released
+| May 21, 2020 | [Unifying Deep Local and Global Features for Image Search (DELG)](https://github.com/tensorflow/models/tree/master/research/delf#delg) code released
+| May 19, 2020 | [MobileDets: Searching for Object Detection Architectures for Mobile Accelerators](https://github.com/tensorflow/models/tree/master/research/object_detection#may-19th-2020) released
+| May 7, 2020 | [MnasFPN with MobileNet-V2 backbone](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#mobile-models) released for object detection
+| May 1, 2020 | [DELF: DEep Local Features](https://github.com/tensorflow/models/tree/master/research/delf) updated to support TensorFlow 2.1
+| 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)) |
+
+## [Milestones](https://github.com/tensorflow/models/milestones)
+
+| Date | Milestone |
+|------|-----------|
+| July 7, 2020 | [![GitHub milestone](https://img.shields.io/github/milestones/progress/tensorflow/models/1)](https://github.com/tensorflow/models/milestone/1) |
+
+## Contributions
+
+[![help wanted:paper implementation](https://img.shields.io/github/issues/tensorflow/models/help%20wanted%3Apaper%20implementation)](https://github.com/tensorflow/models/labels/help%20wanted%3Apaper%20implementation)
+
+If you want to contribute, please review the [contribution guidelines](https://github.com/tensorflow/models/wiki/How-to-contribute).
+
+## License
+
+[Apache License 2.0](LICENSE)

models-2.13.1/docs/README.md

+# Public docs for TensorFlow Models
+
+This directory contains the top-level public documentation for
+[TensorFlow Models](https://github.com/tensorflow/models)
+
+This directory is mirrored to https://tensorflow.org/tfmodels, and is mainly
+concerned with documenting the tools provided in the `tensorflow_models` pip
+package (including `orbit`).
+
+Api-reference pages are
+[available on the site](https://www.tensorflow.org/api_docs/more).
+
+The
+[Official Models](https://github.com/tensorflow/models/blob/master/official/projects)
+and [Research Models](https://github.com/tensorflow/models/blob/master/research)
+directories are not described in detail here, refer to the individual project
+directories for more information.

models-2.13.1/docs/index.md

+# Model Garden overview
+
+The TensorFlow Model Garden provides implementations of many state-of-the-art
+machine learning (ML) models for vision and natural language processing (NLP),
+as well as workflow tools to let you quickly configure and run those models on
+standard datasets. Whether you are looking to benchmark performance for a
+well-known model, verify the results of recently released research, or extend
+existing models, the Model Garden can help you drive your ML research and
+applications forward.
+
+The Model Garden includes the following resources for machine learning
+developers:
+
+-   [**Official models**](#official) for vision and NLP, maintained by Google
+    engineers
+-   [**Research models**](#research) published as part of ML research papers
+-   [**Training experiment framework**](#training_framework) for fast,
+    declarative training configuration of official models
+-   [**Specialized ML operations**](#ops) for vision and natural language
+    processing (NLP)
+-   [**Model training loop**](#orbit) management with Orbit
+
+These resources are built to be used with the TensorFlow Core framework and
+integrate with your existing TensorFlow development projects. Model
+Garden resources are also provided under an [open
+source](https://github.com/tensorflow/models/blob/master/LICENSE) license, so
+you can freely extend and distribute the models and tools.
+
+Practical ML models are computationally intensive to train and run, and may
+require accelerators such as Graphical Processing Units (GPUs) and Tensor
+Processing Units (TPUs). Most of the models in Model Garden were trained on
+large datasets using TPUs. However, you can also train and run these models on
+GPU and CPU processors.
+
+## Model Garden models
+
+The machine learning models in the Model Garden include full code so you can
+test, train, or re-train them for research and experimentation. The Model Garden
+includes two primary categories of models: *official models* and *research
+models*.
+
+### Official models {:#official}
+
+The [Official Models](https://github.com/tensorflow/models/tree/master/official)
+repository is a collection of state-of-the-art models, with a focus on
+vision and natural language processing (NLP).
+These models are implemented using current TensorFlow 2.x high-level
+APIs. Model libraries in this repository are optimized for fast performance and
+actively maintained by Google engineers. The official models include additional
+metadata you can use to quickly configure experiments using the Model Garden
+[training experiment framework](#training_framework).
+
+### Research models {:#research}
+
+The [Research Models](https://github.com/tensorflow/models/tree/master/research)
+repository is a collection of models published as code resources for research
+papers. These models are implemented using both TensorFlow 1.x and 2.x. Model
+libraries in the research folder are supported by the code owners and the
+research community.
+
+## Training experiment framework {:#training_framework}
+
+The Model Garden training experiment framework lets you quickly assemble and run
+training experiments using its official models and standard datasets. The
+training framework uses additional metadata included with the Model Garden's
+official models to allow you to configure models quickly using a declarative
+programming model. You can define a training experiment using Python commands in
+the
+[TensorFlow Model library](https://www.tensorflow.org/api_docs/python/tfm/core)
+or configure training using a YAML configuration file, like this
+[example](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnet50_tpu.yaml).
+
+The training framework uses
+[`tfm.core.base_trainer.ExperimentConfig`](https://www.tensorflow.org/api_docs/python/tfm/core/base_trainer/ExperimentConfig)
+as the configuration object, which contains the following top-level
+configuration objects:
+
+-   [`runtime`](https://www.tensorflow.org/api_docs/python/tfm/core/base_task/RuntimeConfig):
+    Defines the processing hardware, distribution strategy, and other
+    performance optimizations
+-   [`task`](https://www.tensorflow.org/api_docs/python/tfm/core/config_definitions/TaskConfig):
+    Defines the model, training data, losses, and initialization
+-   [`trainer`](https://www.tensorflow.org/api_docs/python/tfm/core/base_trainer/TrainerConfig):
+    Defines the optimizer, training loops, evaluation loops, summaries, and
+    checkpoints
+
+For a complete example using the Model Garden training experiment framework, see
+the [Image classification with Model Garden](vision/image_classification.ipynb)
+tutorial. For information on the training experiment framework, check out the
+[TensorFlow Models API documentation](https://tensorflow.org/api_docs/python/tfm/core).
+If you are looking for a solution to manage training loops for your model
+training experiments, check out [Orbit](#orbit).
+
+## Specialized ML operations {:#ops}
+
+The Model Garden contains many vision and NLP operations specifically designed
+to execute state-of-the-art models that run efficiently on GPUs and TPUs. Review
+the TensorFlow Models Vision library API docs for a list of specialized
+[vision operations](https://www.tensorflow.org/api_docs/python/tfm/vision).
+Review the TensorFlow Models NLP Library API docs for a list of
+[NLP operations](https://www.tensorflow.org/api_docs/python/tfm/nlp). These
+libraries also include additional utility functions used for vision and NLP data
+processing, training, and model execution.
+
+## Training loops with Orbit {:#orbit}
+
+There are two default options for training TensorFlow models:
+
+* Use the high-level Keras
+[Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit)
+function. If your model and training procedure fit the assumptions of Keras'
+`Model.fit` (incremental gradient descent on batches of data) method this can
+be very convenient.
+* Write a custom training loop
+[with keras](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch),
+or [without](https://www.tensorflow.org/guide/core/logistic_regression_core).
+You can write a custom training loop with low-level TensorFlow methods such as
+`tf.GradientTape` or `tf.function`. However, this approach requires a lot of
+boilerplate code, and doesn't do anything to simplify distributed training.
+
+Orbit tries to provide a third option in between these two extremes.
+
+Orbit is a flexible, lightweight library designed to make it easier to
+write custom training loops in TensorFlow 2.x, and works well with the Model
+Garden [training experiment framework](#training_framework). Orbit handles
+common model training tasks such as saving checkpoints, running model
+evaluations, and setting up summary writing. It seamlessly integrates with
+`tf.distribute` and supports running on different device types, including CPU,
+GPU, and TPU hardware. The Orbit tool is also [open
+source](https://github.com/tensorflow/models/blob/master/orbit/LICENSE), so you
+can extend and adapt to your model training needs.
+
+The Orbit guide is available [here](orbit/index.ipynb).
+
+Note: You can customize how the Keras API executes training. Mainly you must
+override the `Model.train_step` method or use `keras.callbacks` like
+`callbacks.ModelCheckpoint` or `callbacks.TensorBoard`. For more information
+about modifying the behavior of `train_step`, check out the
+[Customize what happens in Model.fit](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit)
+page.