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---
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}
}
``` |