onnx RyzenAI vision classification pytorch dataset:imagenet-1k arxiv:1904.09730 license:apache-2.0 region:us
---
license: apache-2.0
datasets:
- imagenet-1k
metrics:
- accuracy
tags:
- RyzenAI
- vision
- classification
- pytorch
---

# ESE_VoVNet39b
Quantized ESE_VoVNet39b model that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com/en/latest/).


## Model description
VoVNet was first introduced in the paper [An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection](https://arxiv.org/abs/1904.09730). Pretrained on ImageNet-1k in timm by Ross Wightman using RandAugment RA recipe.

The model implementation is from [timm](https://huggingface.co/timm/ese_vovnet39b.ra_in1k).


## 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 [ImageNet](https://huggingface.co/datasets/imagenet-1k) to prepare dataset.

### Model Evaluation

```python
python eval_onnx.py --onnx_model ese_vovnet39b_int.onnx --ipu --provider_config Path\To\vaip_config.json --data_dir /Path/To/Your/Dataset
```

### Performance

|Metric |Accuracy on IPU|
| :----:  | :----: |
|Top1/Top5| 78.96% / 94.53%|


```bibtex
@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```

```bibtex
@inproceedings{lee2019energy,
  title = {An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection},
  author = {Lee, Youngwan and Hwang, Joong-won and Lee, Sangrok and Bae, Yuseok and Park, Jongyoul},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year = {2019}
}

```