onnx RyzenAI vision classification pytorch dataset:imagenet-1k arxiv:1602.07360 license:apache-2.0 region:us
---
license: apache-2.0
datasets:
- imagenet-1k
metrics:
- accuracy
tags:
- RyzenAI
- vision
- classification
- pytorch
---

# SqueezeNet1_1
Quantized SqueezeNet1_1 model that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com/en/latest/).


## Model description
SqueezeNet was first introduced in the paper [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360). This model is SqueezeNet v1.1 which requires 2.4x less computation than SqueezeNet v1.0 without diminshing accuracy.

The model implementation is from [torchvision](https://pytorch.org/vision/main/models/squeezenet.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 SqueezeNet_int8.onnx --ipu --provider_config Path\To\vaip_config.json --data_dir /Path/To/Your/Dataset
```

### Performance

|Metric |Accuracy on IPU|
| :----:  | :----: |
|Top1/Top5| 57.70% / 80.27% |


```bibtex
 @article{SqueezeNet,
    Author = {Forrest N. Iandola and Song Han and Matthew W. Moskewicz and Khalid Ashraf and William J. Dally and Kurt Keutzer},
    Title = {SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $<$0.5MB model size},
    Journal = {arXiv:1602.07360},
    Year = {2016}
}

```