| User | hitch22 |
| Upload Date | July 18 2025 11:23 AM |
| Views | 16 |
| AI Information | |
|---|---|
| Framework | OpenVINO |
| Backend | NPU |
| Device | Intel(R) AI Boost |
| System Information | |
|---|---|
| Operating System | Microsoft Windows 11 Pro (64-bit) |
| Model | Micro-Star International Co., Ltd. Vector 16 HX AI A2XWIG |
| Motherboard | Micro-Star International Co., Ltd. MS-15M3 |
| Power Plan | Balanced |
| CPU Information | |
|---|---|
| Name | Intel(R) Core(TM) Ultra 9 275HX |
| Topology | 1 Processor, 24 Cores |
| Identifier | GenuineIntel Family 6 Model 198 Stepping 2 |
| Base Frequency | 2.70 GHz |
| Cluster 1 | 8 Cores |
| Cluster 2 | 16 Cores |
| Memory Information | |
|---|---|
| Size | 16.00 GB |
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
5817
1.08 KIPS |
|
|
Image Classification (HP)
|
100% |
7083
1.32 KIPS |
|
|
Image Classification (Q)
|
100% |
9722
1.81 KIPS |
|
|
Image Segmentation (SP)
|
100% |
7660
124.2 IPS |
|
|
Image Segmentation (HP)
|
100% |
2913
47.2 IPS |
|
|
Image Segmentation (Q)
|
99% |
3941
63.9 IPS |
|
|
Pose Estimation (SP)
|
100% |
13343
15.6 IPS |
|
|
Pose Estimation (HP)
|
100% |
34018
39.7 IPS |
|
|
Pose Estimation (Q)
|
96% |
75219
88.1 IPS |
|
|
Object Detection (SP)
|
100% |
5440
431.5 IPS |
|
|
Object Detection (HP)
|
100% |
6201
491.9 IPS |
|
|
Object Detection (Q)
|
87% |
9893
794.0 IPS |
|
|
Face Detection (SP)
|
100% |
15272
181.5 IPS |
|
|
Face Detection (HP)
|
100% |
16069
190.9 IPS |
|
|
Face Detection (Q)
|
100% |
31860
378.6 IPS |
|
|
Depth Estimation (SP)
|
100% |
13505
104.0 IPS |
|
|
Depth Estimation (HP)
|
99% |
17817
137.3 IPS |
|
|
Depth Estimation (Q)
|
88% |
33130
257.8 IPS |
|
|
Style Transfer (SP)
|
100% |
25025
32.2 IPS |
|
|
Style Transfer (HP)
|
100% |
49602
63.8 IPS |
|
|
Style Transfer (Q)
|
98% |
93592
120.7 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
8438
311.6 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
14166
523.1 IPS |
|
|
Image Super-Resolution (Q)
|
99% |
23174
858.2 IPS |
|
|
Text Classification (SP)
|
100% |
4185
5.59 KIPS |
|
|
Text Classification (HP)
|
100% |
2025
2.70 KIPS |
|
|
Text Classification (Q)
|
92% |
2050
2.75 KIPS |
|
|
Machine Translation (SP)
|
100% |
3214
55.4 IPS |
|
|
Machine Translation (HP)
|
100% |
4331
74.6 IPS |
|
|
Machine Translation (Q)
|
100% |
4415
76.0 IPS |