Model Accuracy¶
The following two tables present the absolute accuracy drop calculated as the accuracy difference between OV-accuracy and the original frame work accuracy for FP32, and the same for INT8 and FP16 representations of a model on three platform architectures. Please also refer to notes below table for more information.
A - Intel® Core™ i9-9000K (AVX2), INT8 and FP32
B - Intel® Xeon® 6338, (VNNI), INT8 and FP32
C - Intel® Flex-170, INT8 and FP16
OpenVINO™ Model name |
dataset |
Metric Name |
A, INT8 |
B, INT8 |
C, INT8 |
|---|---|---|---|---|---|
bert-base-cased |
SST-2_bert_cased_padded |
accuracy |
-0.76% |
2.42% |
2.72% |
bert-large-uncased-whole-word-masking-squad-0001 |
SQUAD_v1_1_bert_msl384_mql64_ds128_lowercase |
F1 |
0.07% |
-0.03% |
0.11% |
deeplabv3 |
VOC2012_segm |
mean_iou |
0.49% |
0.23% |
-0.16% |
efficientdet-d0 |
COCO2017_detection_91cl |
coco_precision |
-0.84% |
-0.59% |
-0.63% |
faster_rcnn_resnet50_coco |
COCO2017_detection_91cl_bkgr |
coco_orig_precision |
-0.19% |
-0.19% |
-0.04% |
mobilenet-v2 |
ImageNet2012 |
accuracy @ top1 |
-0.97% |
-0.95% |
|
resnet-50 |
ImageNet2012 |
accuracy @ top1 |
-0.09% |
-0.12% |
-0.19% |
ssd-mobilenet-v1-coco |
COCO2017_detection_80cl_bkgr |
coco-precision |
-2.97% |
-0.29% |
-0.26% |
ssd-resnet34-1200 |
COCO2017_detection_80cl_bkgr |
map |
-0.03% |
-0.06% |
0.04% |
unet-camvid-onnx-0001 |
CamVid_12cl |
mean_iou @ mean |
-6.32% |
6.40% |
6.40% |
yolo_v3 |
COCO2017_detection_80cl |
map |
-0.13% |
-0.26% |
-0.44% |
yolo_v3_tiny |
COCO2017_detection_80cl |
map |
-0.11% |
-0.13% |
-0.15% |
yolo_v8n |
COCO2017_detection_80cl |
map |
0.27% |
0.23% |
0.17% |
chatGLM2-6b |
lambada openai |
ppl |
17.595 |
||
Llama-2-7b-chat |
Wiki, StackExch, Crawl |
ppl |
3.268 |
||
Stable-Diffusion-V2-1 |
LIAON-5B |
ppl |
OpenVINO™ Model name |
dataset |
Metric Name |
A, FP32 |
B, FP32 |
C, FP16 |
|---|---|---|---|---|---|
bert-base-cased |
SST-2_bert_cased_padded |
accuracy |
0.00% |
0.00% |
0.00% |
bert-large-uncased-whole-word-masking-squad-0001 |
SQUAD_v1_1_bert_msl384_mql64_ds128_lowercase |
F1 |
0.04% |
0.04% |
0.04% |
deeplabv3 |
VOC2012_segm |
mean_iou |
0.00% |
0.00% |
0.00% |
efficientdet-d0 |
COCO2017_detection_91cl |
coco_precision |
-0.02% |
-0.02% |
-0.02% |
faster_rcnn_resnet50_coco |
COCO2017_detection_91cl_bkgr |
coco_orig_precision |
0.00% |
0.00% |
|
mobilenet-v2 |
ImageNet2012 |
accuracy @ top1 |
0.00% |
0.00% |
0.00% |
resnet-50 |
ImageNet2012 |
accuracy @ top1 |
0.00% |
0.00% |
0.00% |
ssd-mobilenet-v1-coco |
COCO2017_detection_80cl_bkgr |
coco-precision |
0.01% |
0.01% |
0.01% |
ssd-resnet34-1200 |
COCO2017_detection_80cl_bkgr |
map |
0.00% |
0.00% |
0.00% |
unet-camvid-onnx-0001 |
CamVid_12cl |
mean_iou @ mean |
0.00% |
0.00% |
0.00% |
yolo_v3 |
COCO2017_detection_80cl |
map |
0.00% |
0.00% |
0.00% |
yolo_v3_tiny |
COCO2017_detection_80cl |
map |
-0.04% |
-0.04% |
0.02% |
yolo_v8n |
COCO2017_detection_80cl |
map |
0.00% |
0.00% |
0.00% |
chatGLM2-6b |
lambada-openai |
ppl |
17.488 |
||
Llama-2-7b-chat |
Wiki, StackExch, Crawl |
ppl |
3.262 |
||
Stable-Diffusion-V2-1 |
LIAON-5B |
ppl |
Notes: For all accuracy metrics except perplexity a “-“, (minus sign), indicates an accuracy drop. For perplexity the values do not indicate a deviation from a reference but are the actual measured accuracy for the model.