Model Accuracy#
The following two tables present the absolute accuracy drop calculated as the accuracy difference between OV-accuracy and the original framework accuracy for FP32, and the same for INT8, BF16, and FP16 representations of a model on three platform architectures. The third table presents the GenAI model accuracies as absolute accuracy values. Refer to notes below the table for more information.
A - Intel® Core™ i9-9000K (AVX2), INT8 and FP32
B - Intel® Xeon® 6338, (VNNI), INT8 and FP32
C - Intel® Xeon 8480+ (VNNI, AMX), INT8, BF16, FP32
D - Intel® Flex-170, INT8 and FP16
OpenVINO™ Model name |
dataset |
Metric Name |
A, INT8 |
B, INT8 |
C, INT8 |
D, INT8 |
---|---|---|---|---|---|---|
bert-base-cased |
SST-2_bert_cased_padded |
3.33% |
3.22% |
3.05% |
2.88% |
|
bert-large-uncased-whole-word-masking-squad-0001 |
SQUAD_v1_1_bert_msl384_mql64_ds128_lowercase |
F1 |
0.12% |
0.03% |
0.03% |
0.28% |
efficientdet-d0 |
COCO2017_detection_91cl |
coco_precision |
0.00% |
-0.52% |
-0.54% |
-0.60% |
mask_rcnn_resnet50_atrous_coco |
COCO2017_detection_91cl_bkgr |
coco_orig_precision |
0.05% |
0.03% |
0.08% |
-0.09% |
mobilenet-v2 |
ImageNet2012 |
accuracy @ top1 |
-0.87% |
-0.88% |
-0.88% |
|
resnet-50 |
ImageNet2012 |
accuracy @ top1 |
-0.17% |
-0.18% |
-0.18% |
-0.16% |
ssd-resnet34-1200 |
COCO2017_detection_80cl_bkgr |
map |
-0.03% |
-0.02% |
-0.03% |
0.02% |
ssd-mobilenet-v1-coco |
COCO2017_detection_80cl_bkgr |
coco-precision |
-2.74% |
-0.11% |
-0.13% |
-0.12% |
unet-camvid-onnx-0001 |
CamVid_12cl |
mean_iou @ mean |
-6.28% |
6.45% |
6.46% |
6.43% |
yolo_v5m |
COCO2017_detection_80cl |
map |
-0.40% |
-0.32% |
-0.32% |
-0.31% |
yolo_v8n |
COCO2017_detection_80cl |
map |
-0.01% |
-0.04% |
-0.07% |
0.05% |
OpenVINO™ Model name |
dataset |
Metric Name |
A, FP32 |
B, FP32 |
C, FP32 |
C, BF16 |
D, FP16 |
---|---|---|---|---|---|---|---|
bert-base-cased |
SST-2_bert_cased_padded |
0.00% |
0.00% |
0.00% |
-0.01% |
0.01% |
|
bert-large-uncased-whole-word-masking-squad-0001 |
SQUAD_v1_1_bert_msl384_mql64_ds128_lowercase |
F1 |
0.04% |
0.04% |
0.06% |
0.06% |
0.04% |
efficientdet-d0 |
COCO2017_detection_91cl |
coco_precision |
0.01% |
-0.02% |
0.01% |
0.00% |
-0.02% |
mask_rcnn_resnet50_atrous_coco |
COCO2017_detection_91cl_bkgr |
coco_orig_precision |
-0.01% |
-0.01% |
-0.01% |
-0.05% |
0.00% |
mobilenet-v2 |
ImageNet2012 |
accuracy @ top1 |
0.00% |
0.00% |
0.00% |
-0.18% |
0.02% |
resnet-50 |
ImageNet2012 |
accuracy @ top1 |
0.00% |
0.00% |
0.00% |
-0.01% |
-0.01% |
ssd-resnet34-1200 |
COCO2017_detection_80cl_bkgr |
map |
0.02% |
0.00% |
0.00% |
-0.02% |
0.04% |
ssd-mobilenet-v1-coco |
COCO2017_detection_80cl_bkgr |
coco-precision |
-0.08% |
0.01% |
0.01% |
0.08% |
0.01% |
unet-camvid-onnx-0001 |
CamVid_12cl |
mean_iou @ mean |
0.00% |
0.00% |
0.00% |
-0.03% |
-0.03% |
yolo_v5m |
COCO2017_detection_80cl |
map |
0.00% |
0.05% |
0.05% |
0.07% |
0.07% |
yolo_v8n |
COCO2017_detection_80cl |
map |
0.00% |
0.00% |
0.01% |
0.05% |
0.00% |
OpenVINO™ Model name |
dataset |
Metric Name |
A, VNNI-FP16 |
B, VNNI-INT4 |
C, FAMX-FP16 |
D, MTL-INT4 |
---|---|---|---|---|---|---|
chatGLM2-6b |
Wikiset |
ppl |
5.24 |
6.03 |
5.24 |
6.03 |
Falcon-7b-instruct |
Wikitext |
ppl |
1.65 |
1.76 |
1.65 |
1.76 |
Llama-2-7b-chat |
Wikiset |
ppl |
1.58 |
1.59 |
1.91 |
1.59 |
Llama-3-8b |
Wikiset |
ppl |
1.54 |
1.56 |
1.17 |
1.57 |
Mistral-7b |
Wikitext |
ppl |
1.48 |
1.49 |
1.39 |
1.49 |
Phi3-mini-4k-instruct |
Wikitext |
ppl |
1.52 |
1.56 |
1.52 |
1.56 |
Notes: For all accuracy metrics a “-”, (minus sign), indicates an accuracy drop. For perplexity (ppl) the values do not indicate a deviation from a reference but are the actual measured accuracy for the model.