---
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}
}

```