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The ONNX Model Zoo is a collection of pre-trained models for state of the art models in deep learning, available in the ONNX format

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NOTICE: THIS REPO IS DEPRECATED! model-zoo has been merge into [onnx/models](https://github.com/onnx/models). 
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# Open Neural Network eXchange (ONNX) Model Zoo

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The ONNX Model Zoo is a collection of pre-trained models for state-of-the-art models in deep learning, available in the ONNX format. Accompanying each model are [Jupyter](http://jupyter.org) notebooks for model training and running inference with the trained model. The notebooks are written in Python and include links to the training dataset as well as references to the original paper that describes the model architecture. The notebooks can be exported and run as python(.py) files.

## What is ONNX?
The Open Neural Network eXchange ([ONNX](http://onnx.ai)) is a open format to represent deep learning models. With ONNX, developers can move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.

## Models

### Image Classification
This collection of models take images as input, then classifies the major objects in the images into a set of predefined classes.

|Model Class |Reference |Description |
|-|-|-|
|[MobileNet](models/image_classification/mobilenet/)|[Sandler et al.](https://arxiv.org/abs/1801.04381)|Efficient CNN model for mobile and embedded vision applications. Top-5 error from paper - ~10%|
|[ResNet](models/image_classification/resnet/)|[He et al.](https://arxiv.org/abs/1512.03385), [He et al.](https://arxiv.org/abs/1603.05027)|Very deep CNN model (up to 152 layers), won the ImageNet Challenge in 2015. Top-5 error from paper - ~6%|
|[SqueezeNet](models/image_classification/squeezenet/)|[Iandola et al.](https://arxiv.org/abs/1602.07360)|A light-weight CNN providing Alexnet level accuracy with 50X fewer parameters. Top-5 error from paper - ~20%|
|[VGG](models/image_classification/vgg/)|[Simonyan et al.](https://arxiv.org/abs/1409.1556)|Deep CNN model (upto 19 layers) which won the ImageNet Challenge in 2014. Top-5 error from paper - ~8%|
<hr>

### Face Detection and Recognition
These models detect and/or recognize human faces in images. Some more popular models are used for detection/recognition of celebrity faces, gender, age, and emotions.

<hr>

### Object Detection & Segmentation
These models detect the presence of multiple objects in an image and segment out areas of the image where the objects are detected.

<hr>

### Semantic Segmentation
Semantic segmentation models will identify multiple classes of objects in an image and provide information on the areas of the image that object was detected.

|Model Class |Reference |Description |
|-|-|-|
|FCN|[Long et al.](https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf)|[contribute](contribute.md)|

<hr>

### Super Resolution

### Gender Detection

### Machine Translation

### Speech Processing

<hr>

### Language Modelling

### Visual Question Answering & Dialog

<hr>

### Other interesting models

|Model Class |Reference |Description |
|-|-|-|
|Text to Image|	[Generative Adversarial Text to image Synthesis ](https://arxiv.org/abs/1605.05396)|[contribute](contribute.md)|
|Sound Generative models|	[WaveNet: A Generative Model for Raw Audio ](https://arxiv.org/abs/1609.03499)|[contribute](contribute.md)|
|Time Series Forecasting|	[Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks ](https://arxiv.org/pdf/1703.07015.pdf)|[contribute](contribute.md)|
|Recommender systems|[DropoutNet: Addressing Cold Start in Recommender Systems](http://www.cs.toronto.edu/~mvolkovs/nips2017_deepcf.pdf)|[contribute](contribute.md)|
|Collaborative filtering||[contribute](contribute.md)|
|Autoencoders||[contribute](contribute.md)|

<hr>

## Model Visualization
You can see visualizations of each model's network architecture by using [Netron](https://lutzroeder.github.io/Netron).

## Contributions
Do you want to contribute a model? To get started, pick any model presented above with the [contribute](contribute.md) link under the Description column. The links point to a page containing guidelines for making a contribution.

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