readme: ---
  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',
  )
  ```
  