@@ -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) |
| [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) |
