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---
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',
)
```
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