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