| --- |
| 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} |
| } |
| |
| ``` |
