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--- |
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license: apache-2.0 |
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datasets: |
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- imagenet-1k |
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metrics: |
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- accuracy |
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tags: |
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- RyzenAI |
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- vision |
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- classification |
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- pytorch |
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--- |
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# SqueezeNet1_1 |
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Quantized SqueezeNet1_1 model that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com/en/latest/). |
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## Model description |
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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. |
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The model implementation is from [torchvision](https://pytorch.org/vision/main/models/squeezenet.html). |
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## How to use |
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### Installation |
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Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI. |
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Run the following script to install pre-requisites for this model. |
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```bash |
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pip install -r requirements.txt |
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``` |
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### Data Preparation |
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Follow [PyTorch Example](https://github.com/pytorch/examples/blob/main/imagenet/README.md#requirements) to prepare dataset. |
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### Model Evaluation |
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```python |
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python eval_onnx.py --onnx_model SqueezeNet_int8.onnx --ipu --provider_config Path\To\vaip_config.json --data_dir /Path/To/Your/Dataset |
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``` |
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### Performance |
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|Metric |Accuracy on IPU| |
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| :----: | :----: | |
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|Top1/Top5| 57.70% / 80.27% | |
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```bibtex |
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@article{SqueezeNet, |
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Author = {Forrest N. Iandola and Song Han and Matthew W. Moskewicz and Khalid Ashraf and William J. Dally and Kurt Keutzer}, |
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Title = {SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $<$0.5MB model size}, |
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Journal = {arXiv:1602.07360}, |
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Year = {2016} |
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} |
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``` |
