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--- |
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license: apache-2.0 |
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datasets: |
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- imagenet-1k |
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metrics: |
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- accuracy |
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tags: |
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- RyzenAI |
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- vision |
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- classification |
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- pytorch |
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--- |
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# ResNet-50 v1.5 |
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Quantized ResNet model that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com/en/latest/). |
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## Model description |
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ResNet (Residual Network) was first introduced in the paper Deep Residual Learning for Image Recognition by He et al. |
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This model is ResNet50 v1.5 from [torchvision](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet50.html). |
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## How to use |
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### Installation |
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Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI. |
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Run the following script to install pre-requisites for this model. |
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```bash |
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pip install -r requirements.txt |
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``` |
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### Data Preparation |
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Follow [PyTorch Example](https://github.com/pytorch/examples/blob/main/imagenet/README.md#requirements) to prepare dataset. |
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### Model Evaluation |
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```python |
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python eval_onnx.py --onnx_model ResNet_int.onnx --ipu --provider_config Path\To\vaip_config.json --data_dir /Path/To/Your/Dataset |
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``` |
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### Performance |
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|Metric |Accuracy on IPU| |
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| :----: | :----: | |
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|Top1/Top5| 76.17% / 92.86%| |
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```bibtex |
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@article{He2015, |
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author={Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, |
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title={Deep Residual Learning for Image Recognition}, |
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journal={arXiv preprint arXiv:1512.03385}, |
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year={2015} |
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} |
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``` |
