ONNX Model Zoo: A Treasure Trove of Pre-Trained Machine Learning Models
ONNX (Open Neural Network Exchange) has become a widely adopted open standard format for representing machine learning models. With its growing popularity, the ONNX Model Zoo has emerged as a valuable resource for developers and researchers looking for pre-trained, state-of-the-art models in the ONNX format.
The ONNX Model Zoo is a collaborative effort, with contributions from community members who have trained and shared their models. This blog post will provide an overview of the ONNX Model Zoo, its contents, and the benefits it offers to the machine learning community.
What is the ONNX Model Zoo?
The ONNX Model Zoo is a collection of pre-trained machine learning models that are stored in the ONNX format. These models have been contributed by community members and cover a wide range of domains, including image classification, object detection, semantic segmentation, face detection, audio processing, natural language processing, and more.
Each model in the ONNX Model Zoo is accompanied by Jupyter notebooks that provide instructions for training and running inference with the model. These notebooks are written in Python and include links to the training dataset and references to the original research papers that describe the model architecture.
Exploring the ONNX Model Zoo
The ONNX Model Zoo offers a diverse set of models that cater to different machine learning tasks. Let’s take a closer look at some of the categories and models available in the ONNX Model Zoo:
This category of models takes images as input and classifies the major objects in the images into 1000 object categories. From common objects like keyboards, mice, and pencils to various animals, these models provide accurate and efficient classification results.
Domain-Specific Image Classification
In addition to general image classification models, the ONNX Model Zoo also includes models that are specifically trained for certain domains and datasets. These models excel at classifying images in specific contexts, such as medical imaging or satellite imagery.
Object Detection and Semantic Segmentation
Object detection models in the ONNX Model Zoo are capable of detecting the presence of multiple objects in an image and segmenting out the areas where the objects are detected. On the other hand, semantic segmentation models partition an input image by labeling each pixel into a set of pre-defined categories.
Face Detection and Analysis
Face detection models in the ONNX Model Zoo can identify and/or recognize human faces and emotions in given images. Additionally, there are models available for body and gesture analysis, which can identify gender and age in a given image.
Image manipulation models in the ONNX Model Zoo utilize neural networks to transform input images into modified output images. These models are often used for tasks like style transfer or enhancing images by increasing resolution.
The ONNX Model Zoo also includes models that use audio data to train models capable of identifying voice, generating music, or even reading text out loud. These models have applications in speech recognition, music generation, and text-to-speech systems.
Natural Language Processing
There are several subsets of natural language processing models available in the ONNX Model Zoo:
– Question Answering: These models can answer questions about a given context paragraph, making them valuable tools for information retrieval systems.
– Machine Translation: This subset of models learns how to translate input text from one language to another, enabling cross-language communication.
– Language Representation Learning: These models learn representations of language from large corpuses of text, which can be used for various downstream tasks like sentiment analysis or text classification.
– Visual Question Answering: This subset of models uses input images to answer questions about those images, bridging the gap between visual and textual information.
The ONNX Model Zoo also welcomes models that do not fit into the predefined categories. This category encompasses interesting deep learning models that have unique applications and can contribute to the growing model zoo.
Using the ONNX Models
Every ONNX backend should support running the models from the ONNX Model Zoo without any additional configuration. To use a specific model, you can download the ONNX model file from the corresponding GitHub page. The ONNX Model Zoo provides test data files that can be used to validate the models.
If you are new to ONNX and want to visualize the network architecture of a model, you can use Netron, a popular tool for visualizing neural network models. Netron allows you to explore the layers and connections of the model, providing insights into its inner workings.
Contributing to the ONNX Model Zoo
The ONNX team actively encourages users and researchers to contribute their models to the ONNX Model Zoo. If you have trained a model that you believe would be valuable to the community, you can follow the guidelines provided on the contribute page of the ONNX Model Zoo. By contributing your model, you can help expand the diversity and usefulness of the model zoo.
The ONNX Model Zoo is a treasure trove of pre-trained machine learning models in the ONNX format. With models covering various domains and tasks, the ONNX Model Zoo provides a valuable resource for developers and researchers looking to leverage state-of-the-art models in their projects. Whether you need a model for image classification, object detection, natural language processing, or audio processing, the ONNX Model Zoo has you covered. Explore the model zoo, contribute your own models, and join the growing community of ONNX users and enthusiasts.
ONNX Model Zoo Hosted on GitHub