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%
Bvlc_AlexNet	Krizhevsky et al.	Deep CNN model for Image Classification
Bvlc_GoogleNet	Szegedy et al.	Deep CNN model for Image Classification
Bvlc_reference_CaffeNet	Krizhevsky et al.	Deep CNN model for Image Classification
Bvlc_reference_RCNN_ILSVRC13	Girshick et al.	Deep CNN model for Image Classification
DenseNet121	Huang et al.	Deep CNN model for Image Classification
Inception_v1	Szegedy et al.	Deep CNN model for Image Classification
Inception_v2	Szegedy et al.	Deep CNN model for Image Classification
ShuffleNet	Zhang et al.	Deep CNN model for Image Classification
ZFNet512	Zeiler et al.	Deep CNN model for Image Classification

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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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Semantic Segmentation

Semantic segmentation models partition an input image by labeling each pixel into a set of pre-defined categories.

Model Class	Reference	Description
DUC	Wang et al.	Deep CNN based model with >80% mIOU (mean Intersection Over Union) trained on urban street images
FCN	Long 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
Tiny_YOLOv2	Redmon et al.	Deep CNN model for Object Detection
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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Emotion Recognition

Model Class	Reference	Description
Emotion FerPlus	Barsoum et al.	Deep CNN model for Emotion recognition

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Hand Written Digit Recognition

Model Class	Reference	Description
MNIST- Hand Written Digit Recognition	Convolutional Neural Network with MNIST	Deep CNN model for hand written digit identification

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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.

Usage

Every ONNX backend should support running these models out of the box. After downloading and extracting the tarball of each model, there should be

• A protobuf file model.onnx which is the serialized ONNX model.
• Test data.

The test data are provided in two different formats:

• Serialized Numpy archives, which are files named like test_data_*.npz, each file contains one set of test inputs and outputs. They can be used like this:

import numpy as np
import onnx
import onnx_backend as backend

# Load the model and sample inputs and outputs
model = onnx.load(model_pb_path)
sample = np.load(npz_path, encoding='bytes')
inputs = list(sample['inputs'])
outputs = list(sample['outputs'])

# Run the model with an onnx backend and verify the results
np.testing.assert_almost_equal(outputs, backend.run_model(model, inputs))

• Serialized protobuf TensorProtos, which are stored in folders named like test_data_set_*. They can be used as the following:

import numpy as np
import onnx
import os
import glob
import onnx_backend as backend

from onnx import numpy_helper

model = onnx.load('model.onnx')
test_data_dir = 'test_data_set_0'

# Load inputs
inputs = []
inputs_num = len(glob.glob(os.path.join(test_data_dir, 'input_*.pb')))
for i in range(inputs_num):
    input_file = os.path.join(test_data_dir, 'input_{}.pb'.format(i))
    tensor = onnx.TensorProto()
    with open(input_file, 'rb') as f:
        tensor.ParseFromString(f.read())
    inputs.append(numpy_helper.to_array(tensor))

# Load reference outputs
ref_outputs = []
ref_outputs_num = len(glob.glob(os.path.join(test_data_dir, 'output_*.pb')))
for i in range(ref_outputs_num):
    output_file = os.path.join(test_data_dir, 'output_{}.pb'.format(i))
    tensor = onnx.TensorProto()
    with open(output_file, 'rb') as f:
        tensor.ParseFromString(f.read())
    ref_outputs.append(numpy_helper.to_array(tensor))

# Run the model on the backend
outputs = list(backend.run_model(model, inputs))

# Compare the results with reference outputs.
for ref_o, o in zip(ref_outputs, outputs):
    np.testing.assert_almost_equal(ref_o, o)

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.

License

MIT License