ONNX - Model Zoo
The ONNX Model Zoo is a collection of pre-trained models in the ONNX (Open Neural Network Exchange) format, designed to easily use the machine learning models without the needing to train them from scratch.
Whether you're working with image classification, object detection, natural language processing, or other machine learning tasks, the ONNX Model Zoo provides a variety of models that are ready for inference with ONNX Runtime.
In this tutorial, we will learn about the the ONNX Model Zoo and its offerings across various domains such as computer vision, natural language processing (NLP), generative AI, and graph machine learning.
What is ONNX Model Zoo?
The ONNX Model Zoo is a repository of pre-trained models that are available for download and inference. These models are trained on large datasets and are provided in ONNX format, allowing you to use them across different frameworks and platforms without worrying about model conversion or compatibility.
The ONNX Model Zoo provides state-of-the-art models sourced from prominent open-source repositories like timm, torchvision, torch_hub, and transformers, offering developers and researchers access to pre-trained models that can be directly utilized in AI applications.
Accessing the ONNX Model Zoo
To access the ONNX Model Zoo −
- Visit the ONNX Model Zoo GitHub repository.
- Browse through available models such as MobileNet, ResNet, SqueezeNet, AlexNet, and many others.
- Download pre-trained ONNX models directly from the repository.
These models are ready to be used with ONNX Runtime, allowing you to quickly deploy solutions without needing to train models from scratch.
Key Features of ONNX Model Zoo
- Pre-trained Models: Access a wide range of models that are pre-trained on large datasets, saving time and computational resources.
- Interoperability: Leverage ONNX models across different frameworks like PyTorch, TensorFlow, and more, enhancing cross-platform deployment.
- Ready for Inference: The models are optimized for inference using ONNX Runtime, providing fast and efficient performance across devices and platforms.
- Git LFS: The ONNX Model Zoo files can be larger to handle those files, it uses the Git LFS (Large File Storage) and Git LFS command line for downloading multiple ONNX models.
Categories of ONNX Model Zoo
The ONNX Model Zoo offers models for a wide range of machine learning tasks. Here are the most common categories −
- Computer Vision
- Natural Language Processing (NLP)
- Generative AI
- Graph Machine Learning
Computer Vision
The ONNX Model Zoo offers an extensive set of models modified for computer vision tasks, including −
Image Classification Models
These models classify images into predefined categories. The ONNX Model Zoo provides popular pre-trained models such as −
- MobileNet: A lightweight deep neural network for mobile and embedded vision.
- ResNet: A CNN (up to 152 layers) using shortcut connections for image classification.
- SqueezeNet: A compact CNN model with 50x fewer parameters than AlexNet.
- VGG: A deep CNN with smaller filters, providing high accuracy.
- AlexNet: A classic deep CNN for classifying objects in images.
Object Detection & Image Segmentation
Detect and segment objects in images using models like −
- Tiny YOLOv2 and YOLOv3: Real-time object detection models capable of identifying multiple objects in an image.
- SSD (Single Stage Detector): A fast model for detecting objects in real time.
- Mask-RCNN: A network for instance segmentation, detecting objects and predicting their mask.
Body, Face, and Gesture Analysis
Models in this category are designed to detect and analyze human faces, emotions, and gestures −
- ArcFace: A face recognition model producing embeddings for facial images.
- UltraFace: A lightweight face detection model optimized for edge devices.
- Emotion FerPlus: Detects emotions from facial images.
- Age and Gender Classification: Predicts age and gender from images.
Image Manipulation
These models are designed to modify images through various transformations −
- CycleGAN: Translates images between domains without paired examples (e.g., turning a photo into a painting).
- Super Resolution: Upscales images to higher resolutions using sub-pixel convolution layers.
- Fast Neural Style Transfer: Applies artistic styles to images using a loss network.
Natural Language Processing (NLP)
For NLP tasks, the ONNX Model Zoo offers models for −
- Machine Translation: Translating text from one language to another.
- Machine Comprehension: Understanding and responding to natural language queries.
- Language Modeling: Predicting the likelihood of a sequence of words.
Generative AI
Generative models available in the ONNX Model Zoo include −
- Machine Translation: Translating text from one language to another.
- Machine Comprehension: Understanding and responding to natural language queries.
- Language Modeling: Predicting the likelihood of a sequence of words.
- Visual Question Answering: Combining image recognition and natural language understanding.
- Dialog Systems: Generating conversational responses based on input data.
Graph Machine Learning
Graph-based models are used in machine learning tasks where data is represented as graphs. These models are commonly used in applications like social network analysis, molecular biology, and more.