This repository contains a number of different models implemented in [TensorFlow](https://www.tensorflow.org):
# Welcome to the Model Garden for TensorFlow
The [official models](official) are a collection of example models that use TensorFlow 2's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read. We especially recommend newer TensorFlow users to start here.
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.
The [research models](https://github.com/tensorflow/models/tree/master/research) are a large collection of models implemented in TensorFlow by researchers. They are not officially supported or available in release branches; it is up to the individual researchers to maintain the models and/or provide support on issues and pull requests.
## Structure
| Folder | 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 |
## Contribution guidelines
## Contribution guidelines
If you want to contribute to models, be sure to review the [contribution guidelines](CONTRIBUTING.md).
If you want to contribute to models, please review the [contribution guidelines](CONTRIBUTING.md).
# Offically Supported TensorFlow 2.1 Models on Cloud TPU
# Offically Supported TensorFlow 2.1+ Models on Cloud TPU
## Natural Language Processing
## Natural Language Processing
*[bert](nlp/bert): A powerful pre-trained language representation model:
*[bert](nlp/bert): A powerful pre-trained language representation model:
BERT, which stands for Bidirectional Encoder Representations from
BERT, which stands for Bidirectional Encoder Representations from
Transformers.
Transformers.
[BERT FineTuning with Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/bert-2.x) provides step by step instructions on Cloud TPU training. You can look [Bert MNLI Tensorboard.dev metrics](https://tensorboard.dev/experiment/mIah5lppTASvrHqWrdr6NA) for MNLI fine tuning task.
[BERT FineTuning with Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/bert-2.x) provides step by step instructions on Cloud TPU training. You can look [Bert MNLI Tensorboard.dev metrics](https://tensorboard.dev/experiment/LijZ1IrERxKALQfr76gndA) for MNLI fine tuning task.
*[transformer](nlp/transformer): A transformer model to translate the WMT
*[transformer](nlp/transformer): A transformer model to translate the WMT
English to German dataset.
English to German dataset.
[Training transformer on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/transformer-2.x) for step by step instructions on Cloud TPU training.
[Training transformer on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/transformer-2.x) for step by step instructions on Cloud TPU training.
## Computer Vision
## Computer Vision
*[efficientnet](vision/image_classification): A family of convolutional
neural networks that scale by balancing network depth, width, and
resolution and can be used to classify ImageNet's dataset of 1000 classes.
See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/KnaWjrq5TXGfv0NW5m7rpg/#scalars).
*[mnist](vision/image_classification): A basic model to classify digits
*[mnist](vision/image_classification): A basic model to classify digits
from the MNIST dataset. See [Running MNIST on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/mnist-2.x) tutorial and [Tensorboard.dev metrics](https://tensorboard.dev/experiment/mIah5lppTASvrHqWrdr6NA).
from the MNIST dataset. See [Running MNIST on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/mnist-2.x) tutorial and [Tensorboard.dev metrics](https://tensorboard.dev/experiment/mIah5lppTASvrHqWrdr6NA).
*[mask-rcnn](vision/detection): An object detection and instance segmentation model. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/LH7k0fMsRwqUAcE09o9kPA).
*[resnet](vision/image_classification): A deep residual network that can
*[resnet](vision/image_classification): A deep residual network that can
be used to classify ImageNet's dataset of 1000 classes.
be used to classify ImageNet's dataset of 1000 classes.
See [Training ResNet on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/resnet-2.x) tutorial and [Tensorboard.dev metrics](https://tensorboard.dev/experiment/CxlDK8YMRrSpYEGtBRpOhg).
See [Training ResNet on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/resnet-2.x) tutorial and [Tensorboard.dev metrics](https://tensorboard.dev/experiment/CxlDK8YMRrSpYEGtBRpOhg).
The TensorFlow official models are a collection of models that use
# TensorFlow Official Models
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
The TensorFlow official models are a collection of models
same or improved speed and performance with each new TensorFlow build.
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.
## Tensorflow releases
## Model Implementations
The master branch of the models are **in development** with TensorFlow 2.x, and
### Natural Language Processing
they target the
[nightly binaries](https://github.com/tensorflow/tensorflow#installation) built
from the
[master branch of TensorFlow](https://github.com/tensorflow/tensorflow/tree/master).
You may start from installing with pip:
```shell
| Model | Description | Reference |
pip3 install tf-nightly
| ----- | ----------- | --------- |
```
| [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) |
**Stable versions** of the official models targeting releases of TensorFlow are
### Computer Vision
available as tagged branches or
[downloadable releases](https://github.com/tensorflow/models/releases). Model
repository version numbers match the target TensorFlow release, such that
[release v2.1.0](https://github.com/tensorflow/models/releases/tag/v2.1.0) are
