FPT Elead F22FAH Generic
Geekbench AI Score
1356
Single Precision Score
2001
Half Precision Score
1875
Quantized Score
Geekbench AI 1.2.0 for Windows AVX2
Result Information
| User | rennsport |
| Upload Date | April 18 2025 03:59 AM |
| Views | 2 |
System Information
| AI Information | |
|---|---|
| Framework | OpenVINO |
| Backend | GPU |
| Device | Intel(R) UHD Graphics 630 (iGPU) |
| System Information | |
|---|---|
| Operating System | Microsoft Windows 10 Pro (64-bit) |
| Model | Generic |
| Motherboard | ASRock H470M-HDV/M.2 |
| Power Plan | High performance |
| CPU Information | |
|---|---|
| Name | Intel Core i5-10400 |
| Topology | 1 Processor, 6 Cores, 12 Threads |
| Identifier | GenuineIntel Family 6 Model 165 Stepping 3 |
| Base Frequency | 2.90 GHz |
| Cluster 1 | 6 Cores |
| Memory Information | |
|---|---|
| Size | 16.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
843
156.9 IPS |
|
|
Image Classification (HP)
|
100% |
1333
247.8 IPS |
|
|
Image Classification (Q)
|
100% |
1216
226.1 IPS |
|
|
Image Segmentation (SP)
|
100% |
1125
18.2 IPS |
|
|
Image Segmentation (HP)
|
100% |
1776
28.8 IPS |
|
|
Image Segmentation (Q)
|
99% |
1641
26.7 IPS |
|
|
Pose Estimation (SP)
|
100% |
1936
2.26 IPS |
|
|
Pose Estimation (HP)
|
99% |
2065
2.42 IPS |
|
|
Pose Estimation (Q)
|
97% |
2766
3.24 IPS |
|
|
Object Detection (SP)
|
100% |
800
63.5 IPS |
|
|
Object Detection (HP)
|
99% |
1292
102.8 IPS |
|
|
Object Detection (Q)
|
88% |
1208
96.9 IPS |
|
|
Face Detection (SP)
|
100% |
2497
29.7 IPS |
|
|
Face Detection (HP)
|
100% |
4172
49.6 IPS |
|
|
Face Detection (Q)
|
97% |
3606
43.0 IPS |
|
|
Depth Estimation (SP)
|
100% |
2478
19.1 IPS |
|
|
Depth Estimation (HP)
|
98% |
4177
32.3 IPS |
|
|
Depth Estimation (Q)
|
89% |
3777
29.4 IPS |
|
|
Style Transfer (SP)
|
100% |
5472
7.03 IPS |
|
|
Style Transfer (HP)
|
100% |
7595
9.76 IPS |
|
|
Style Transfer (Q)
|
98% |
6816
8.79 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
1000
36.9 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
1788
66.0 IPS |
|
|
Image Super-Resolution (Q)
|
99% |
1711
63.4 IPS |
|
|
Text Classification (SP)
|
71% |
610
882.0 IPS |
|
|
Text Classification (HP)
|
71% |
713
1.03 KIPS |
|
|
Text Classification (Q)
|
92% |
526
706.8 IPS |
|
|
Machine Translation (SP)
|
100% |
693
11.9 IPS |
|
|
Machine Translation (HP)
|
97% |
970
16.8 IPS |
|
|
Machine Translation (Q)
|
98% |
969
16.8 IPS |
