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 also be exported and run as Python (.py) files.

The Open Neural Network eXchange (ONNX) is an 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.

• Image Classification
• Object Detection & Image Segmentation
• Body, Face & Gesture Analysis
• Image Manipulation
• Speech & Audio Processing
• Machine Comprehension
• Machine Translation
• Language Modelling
• Visual Question Answering & Dialog
• Other interesting models

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

This subset of models classify images for specific domains and datasets.

Object detection models detect the presence of multiple objects in an image and segment out areas of the image where the objects are detected. Semantic segmentation models partition an input image by labeling each pixel into a set of pre-defined categories.

Face detection models identify and/or recognize human faces in images. Some more popular models are used for detection of celebrity faces, gender, age, and emotions.

Image manipulation models use neural networks to transform input images to modified output images. Some popular models in this category involve style transfer or enhancing images by increasing resolution.

This class of models uses audio data to train models that can identify voice, generate music, or even read text out loud.

This subset of natural language processing models that answer questions about a given context paragraph.

This class of natural language processing models learns how to translate input text to another language.

This subset of natural language processing models learns representations of language from large corpuses of text.

This subset of natural language processing models uses input images to answer questions about those images.

There are many interesting deep learning models that do not fit into the categories described above. The ONNX team would like to highly encourage users and researchers to contribute their models to the growing model zoo.

Every ONNX backend should support running the models out of the box. After downloading and extracting the tarball of each model, you will find:

• A protobuf file model.onnx that represents the serialized ONNX model.
• Test data (in the form of serialized protobuf TensorProto files or serialized NumPy archives).

The test data files can be used to validate ONNX models from the Model Zoo. We have provided the following interface examples for you to get started. Please replace onnx_backend in your code with the appropriate framework of your choice that provides ONNX inferencing support, and likewise replace backend.run_model with the framework's model evaluation logic.

There are two different formats for the test data files:

• Serialized protobuf TensorProtos (.pb), stored in folders with the naming convention test_data_set_*.

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)

• Serialized Numpy archives, stored in files with the naming convention test_data_*.npz. Each file contains one set of test inputs and outputs.

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))

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

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.

MIT License