2.1 KiB
2.1 KiB
Model Accuracy and Performance for INT8 and FP32
The following table presents the absolute accuracy drop calculated as the accuracy difference between FP32 and INT8 representations of a model on two platforms
- A - Intel® Core™ i9-9000K (AVX2)
- B - Intel® Xeon® 6338, (VNNI)
- C - Intel® Flex-170
@sphinxdirective .. list-table:: Model Accuracy :header-rows: 1
-
- OpenVINO™ Model name
- dataset
- Metric Name
- A
- B
- C
-
- bert-base-cased
- SST-2_bert_cased_padded
- accuracy
- 0.11%
- 1.15%
- 0.57%
-
- bert-large-uncased-whole-word-masking-squad-0001
- SQUAD_v1_1_bert_msl384_mql64_ds128_lowercase
- F1
- 0.51%
- 0.55%
- 0.68%
-
- deeplabv3
- VOC2012_segm
- mean_iou
- 0.44%
- 0.06%
- 0.04%
-
- densenet-121
- ImageNet2012
- accuracy @ top1
- 0.31%
- 0.32%
- 0.30%
-
- efficientdet-d0
- COCO2017_detection_91cl
- coco_precision
- 0.88%
- 0.62%
- 0.50%
-
- faster_rcnn_resnet50_coco
- COCO2017_detection_91cl_bkgr
- coco_precision
- 0.19%
- 0.19%
- 0.20%
-
- googlenet-v4
- ImageNet2012_bkgr
- accuracy @ top1
- 0.07%
- 0.09%
- 0.26%
-
- mobilenet-ssd
- VOC2007_detection
- map
- 0.47%
- 0.14%
- 0.48%
-
- mobilenet-v2
- ImageNet2012
- accuracy @ top1
- 0.50%
- 0.18%
- 0.20%
-
- resnet-18
- ImageNet2012
- accuracy @ top1
- 0.27%
- 0.24%
- 0.29%
-
- resnet-50
- ImageNet2012
- accuracy @ top1
- 0.13%
- 0.12%
- 0.13%
-
- ssd-resnet34-1200
- COCO2017_detection_80cl_bkgr
- map
- 0.08%
- 0.09%
- 0.06%
-
- unet-camvid-onnx-0001
- CamVid_12cl
- mean_iou @ mean
- 0.33%
- 0.33%
- 0.30%
-
- yolo_v3_tiny
- COCO2017_detection_80cl
- map
- 0.01%
- 0.07%
- 0.12%
-
- yolo_v4
- COCO2017_detection_80cl
- map
- 0.05%
- 0.06%
- 0.01%
@endsphinxdirective