NOTICE: THIS REPO IS DEPRECATED! model-zoo has been merge into onnx/models.
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.
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.
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% |
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 |
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 |
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 |
| Model Class | Reference | Description |
|---|---|---|
| Image Super resolution using deep convolutional networks | Dong et al. | contribute |
| Model Class | Reference | Description |
|---|---|---|
| Age and Gender Classification using Convolutional Neural Networks | Levi et al. | contribute |
| Model Class | Reference | Description |
|---|---|---|
| Unpaired Image to Image Translation using Cycle consistent Adversarial Network | Zhu et al. | contribute |
| 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 |
| 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 |
| Model Class | Reference | Description |
|---|---|---|
| Deep Neural Network Language Models | Arisoy et al. | contribute |
| 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 |
| 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 |
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.