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--- |
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license: apache-2.0 |
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tags: |
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- int8 |
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- Intel® Neural Compressor |
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- PostTrainingStatic |
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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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--- |
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# The INT8 model based on vit-base-patch16-224 which finetuned on imagenet-1k |
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### Post-training static quantization |
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This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). |
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The original fp32 model comes from the fine-tuned model [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224). |
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The calibration dataloader is the train dataloader. The default calibration sampling size 1000 because of 1000 classes of imagenet-1k. |
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The linear modules **vit.encoder.layer.5.output.dense**, **vit.encoder.layer.9.attention.attention.query.module**, fall back to fp32 for less than 1% relative accuracy loss. |
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### Evaluation result |
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| |INT8|FP32| |
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|---|:---:|:---:| |
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| **Accuracy (eval-acc)** |80.576|81.326| |
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| **Model size (MB)** |94|331| |
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### Load with Intel® Neural Compressor: |
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```python |
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from neural_compressor.utils.load_huggingface import OptimizedModel |
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int8_model = OptimizedModel.from_pretrained( |
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'Intel/vit-base-patch16-224-int8-static', |
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) |
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``` |
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