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