jaeyounkim / model-zoo-old

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

NOTICE: THIS REPO IS DEPRECATED! model-zoo has been merge into onnx/models.

Open Neural Network eXchange (ONNX) Model Zoo

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 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) 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	Sandler et al.	Efficient CNN model for mobile and embedded vision applications. Top-5 error from paper - ~10%
ResNet	He et al., He et al.	Very deep CNN model (up to 152 layers), won the ImageNet Challenge in 2015. Top-5 error from paper - ~6%
SqueezeNet	Iandola et al.	A light-weight CNN providing Alexnet level accuracy with 50X fewer parameters. Top-5 error from paper - ~20%
VGG	Simonyan et al.	Deep CNN model (upto 19 layers) which won the ImageNet Challenge in 2014. Top-5 error from paper - ~8%

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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.

Model Class	Reference	Description
ArcFace	Deng et al.	ArcFace is a CNN based model for face recognition which learns discriminative features of faces and produces embeddings for input face images.
CNN Cascade	Li et al.	contribute

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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.

Model Class	Reference	Description
SSD	Liu et al.	contribute
Faster-RCNN	Ren et al.	contribute
Mask-RCNN	He et al.	contribute
YOLO v2	Redmon et al.	contribute
YOLO v3	Redmon et al.	contribute

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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.	contribute

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Super Resolution

Model Class	Reference	Description
Image Super resolution using deep convolutional networks	Dong et al.	contribute

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Gender Detection

Model Class	Reference	Description
Age and Gender Classification using Convolutional Neural Networks	Levi et al.	contribute

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Style Transfer

Model Class	Reference	Description
Unpaired Image to Image Translation using Cycle consistent Adversarial Network	Zhu et al.	contribute

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Machine Translation

Model Class	Reference	Description
Neural Machine Translation by jointly learning to align and translate	Bahdanau et al.	contribute
Google's Neural Machine Translation System	Wu et al.	contribute

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Speech Processing

Model Class	Reference	Description
Speech recognition with deep recurrent neural networks	Graves et al.	contribute
Deep voice: Real time neural text to speech	Arik et al.	contribute

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Language Modelling

Model Class	Reference	Description
Deep Neural Network Language Models	Arisoy et al.	contribute

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Visual Question Answering & Dialog

Model Class	Reference	Description
VQA: Visual Question Answering	Agrawal et al.	contribute
Yin and Yang: Balancing and Answering Binary Visual Questions	Zhang et al.	contribute
Making the V in VQA Matter	Goyal et al.	contribute
Visual Dialog	Das et al.	contribute

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Other interesting models

Model Class	Reference	Description
Text to Image	Generative Adversarial Text to image Synthesis	contribute
Sound Generative models	WaveNet: A Generative Model for Raw Audio	contribute
Time Series Forecasting	Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks	contribute
Recommender systems	DropoutNet: Addressing Cold Start in Recommender Systems	contribute
Collaborative filtering		contribute
Autoencoders		contribute

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Model Visualization

You can see visualizations of each model's network architecture by using Netron.

Contributions

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