ONNX* is a representation format for deep learning models. ONNX allows AI developers easily transfer models between different frameworks that helps to choose the best combination for them. Today, PyTorch*, Caffe2*, Apache MXNet*, Microsoft Cognitive Toolkit* and other tools are developing ONNX support.
| Model Name | Path to Public Models master branch |
|---|---|
| bert_large | model archive |
| bvlc_alexnet | model archive |
| bvlc_googlenet | model archive |
| bvlc_reference_caffenet | model archive |
| bvlc_reference_rcnn_ilsvrc13 | model archive |
| inception_v1 | model archive |
| inception_v2 | model archive |
| resnet50 | model archive |
| squeezenet | model archive |
| densenet121 | model archive |
| emotion_ferplus | model archive |
| mnist | model archive |
| shufflenet | model archive |
| VGG19 | model archive |
| zfnet512 | model archive |
| GPT-2 | model archive |
| YOLOv3 | model archive |
Listed models are built with the operation set version 8 except the GPT-2 model. Models that are upgraded to higher operation set versions may not be supported.
Starting from the R5 release, the OpenVINO™ toolkit officially supports public PaddlePaddle* models via ONNX conversion. The list of supported topologies downloadable from PaddleHub is presented below:
| Model Name | Command to download the model from PaddleHub |
|---|---|
| MobileNetV2 | hub install mobilenet_v2_imagenet==1.0.1 |
| ResNet18 | hub install resnet_v2_18_imagenet==1.0.0 |
| ResNet34 | hub install resnet_v2_34_imagenet==1.0.0 |
| ResNet50 | hub install resnet_v2_50_imagenet==1.0.1 |
| ResNet101 | hub install resnet_v2_101_imagenet==1.0.1 |
| ResNet152 | hub install resnet_v2_152_imagenet==1.0.1 |
NOTE: To convert a model downloaded from PaddleHub use paddle2onnx converter.
The list of supported topologies from the models v1.5 package:
NOTE: To convert these topologies one should first serialize the model by calling
paddle.fluid.io.save_inference_model
(description) command and after that use paddle2onnx converter.
The Model Optimizer process assumes you have an ONNX model that was directly downloaded from a public repository or converted from any framework that supports exporting to the ONNX format.
To convert an ONNX* model:
<INSTALL_DIR>/deployment_tools/model_optimizer directory.mo.py script to simply convert a model with the path to the input model .nnet file: There are no ONNX* specific parameters, so only framework-agnostic parameters are available to convert your model.
Refer to Supported Framework Layers for the list of supported standard layers.