@@ -45,28 +45,41 @@ You can load INT8 model by load_inference_model [API](https://github.com/PaddleP
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## 3. Result
We provide the results of accuracy measurd on [Intel® Xeon® Platinum Gold Processor](https://ark.intel.com/products/120489/Intel-Xeon-Gold-6148-Processor-27-5M-Cache-2-40-GHz-"Intel® Xeon® Gold 6148 Processor")(also known as Intel® Xeon® Skylake6148).
We provide the results of accuracy and performance measured on Intel(R) Xeon(R) Gold 6271 (single core).
**I. Top-1 Accuracy on Intel(R) Xeon(R) Gold 6271**
| ResNet-50 | Full ImageNet Val | 11.54 images/s | 32.2 images/s | 2.79 |
| MobileNet-V1 | Full ImageNet Val | 49.21 images/s | 108.37 images/s | 2.2 |
Please note that [Small](http://paddle-inference-dist.bj.bcebos.com/int8/calibration_test_data.tar.gz"Small") is a subset of [full ImageNet validation dataset](http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_val.tar"full ImageNet validation dataset").
Please note that [full ImageNet validation dataset](http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_val.tar"full ImageNet validation dataset") can be downloaded by script `test_calibration.py` with `DATASET=full`.
Notes:
* The accuracy measurement requires the model with `label`.
* The INT8 theoretical speedup is ~1.33X on Intel® Xeon® Skylake Server (please refer to `This allows for 4x more input at the cost of 3x more instructions or 33.33% more compute` in [Reference](https://software.intel.com/en-us/articles/lower-numerical-precision-deep-learning-inference-and-training"Reference")).
* The INT8 theoretical speedup is 4X on Intel® Xeon® Cascadelake Server (please refer to `The theoretical peak compute gains are 4x int8 OPS over fp32 OPS.` in [Reference](https://software.intel.com/en-us/articles/lower-numerical-precision-deep-learning-inference-and-training"Reference")). Therefore, op-level gain is 4X and topology-level is smaller.
