Merge branch 'zhanggzh-master-patch-16456' into 'master'

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

See merge request zhanggzh/resnet50_tf!1

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

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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.

models-2.13.1/docs/nlp/_guide_toc.yaml

-toc:
-- heading: TensorFlow Models - NLP
-  style: divider
-- title: "Overview"
-  path: /tfmodels/nlp
-- title: "Customize a transformer encoder"
-  path: /tfmodels/nlp/customize_encoder
-- title: "Load LM checkpoints"
-  path: /tfmodels/nlp/load_lm_ckpts