- [List of supported models exportable to ONNX](#list-of-supported-models-exportable-to-onnx)
- [Reminders](#reminders)
- [FAQs](#faqs)
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## How to convert models from Pytorch to ONNX
### Prerequisite
1. Please refer to [install](https://mmclassification.readthedocs.io/en/latest/install.html#install-mmclassification) for installation of MMClassification.
-`--out`: The path of output result file in pickle format.
-`--metrics`: Evaluation metrics, which depends on the dataset, e.g., "accuracy", "precision", "recall", "f1_score", "support" for single label dataset, and "mAP", "CP", "CR", "CF1", "OP", "OR", "OF1" for multi-label dataset.
-`--show`: Determines whether to show classifier outputs. If not specified, it will be set to `False`.
-`--show-dir`: Directory where painted images will be saved
-`--metrics-options`: Custom options for evaluation, the key-value pair in `xxx=yyy` format will be kwargs for `dataset.evaluate()` function
-`--cfg-options`: Override some settings in the used config file, the key-value pair in `xxx=yyy` format will be merged into config file.
### Results and Models
This part selects ImageNet for onnxruntime verification. ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/).
| MobileNetV2 | [mobilenet-v2_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | Y | Y | |
| ResNet | [resnet18_8xb16_cifar10.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet/resnet18_8xb16_cifar10.py) | Y | Y | |
| ResNeXt | [resnext50-32x4d_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | Y | Y | |
| SE-ResNet | [seresnet50_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet/seresnet50_8xb32_in1k.py) | Y | Y | |
| ShuffleNetV1 | [shufflenet-v1-1x_16xb64_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | Y | Y | |
| ShuffleNetV2 | [shufflenet-v2-1x_16xb64_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | Y | Y | |
- *All models above are tested with Pytorch==1.6.0*
## Reminders
- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, please try to dig a little deeper and debug a little bit more and hopefully solve them by yourself.
