Hewlett-Packard HP ProDesk 600 G1 SFF
Geekbench ML Score
832
ONNX CPU Inference Score
Geekbench ML 0.6.0 for Windows AVX2
Result Information
| Upload Date | August 25 2024 07:17 PM |
| Views | 3 |
System Information
| System Information | |
|---|---|
| Operating System | Microsoft Windows 10 Pro (64-bit) |
| Model | Hewlett-Packard HP ProDesk 600 G1 SFF |
| Motherboard | Hewlett-Packard 18E7 |
| Power Plan | Balanced |
| CPU Information | |
|---|---|
| Name | Intel Core i5-4590 |
| Topology | 1 Processor, 4 Cores |
| Identifier | GenuineIntel Family 6 Model 60 Stepping 3 |
| Base Frequency | 3.30 GHz |
| Cluster 1 | 4 Cores |
| L1 Instruction Cache | 32.0 KB x 2 |
| L1 Data Cache | 32.0 KB x 2 |
| L2 Cache | 256 KB x 2 |
| L3 Cache | 6.00 MB x 1 |
| Memory Information | |
|---|---|
| Size | 24.00 GB |
| Inference Information | |
|---|---|
| Framework | ONNX |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
518
96.9 IPS |
|
|
Image Classification (F16)
|
100% |
514
96.3 IPS |
|
|
Image Classification (I8)
|
97% |
626
117.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
391
6.53 IPS |
|
|
Image Segmentation (F16)
|
100% |
391
6.52 IPS |
|
|
Image Segmentation (I8)
|
97% |
634
10.6 IPS |
|
|
Pose Estimation (F32)
|
100% |
1558
1.89 IPS |
|
|
Pose Estimation (F16)
|
100% |
1754
2.12 IPS |
|
|
Pose Estimation (I8)
|
100% |
2109
2.55 IPS |
|
|
Object Detection (F32)
|
100% |
475
35.5 IPS |
|
|
Object Detection (F16)
|
100% |
425
31.7 IPS |
|
|
Object Detection (I8)
|
60% |
340
25.4 IPS |
|
|
Face Detection (F32)
|
100% |
622
7.39 IPS |
|
|
Face Detection (F16)
|
100% |
643
7.64 IPS |
|
|
Face Detection (I8)
|
87% |
1083
12.9 IPS |
|
|
Depth Estimation (F32)
|
100% |
1841
14.3 IPS |
|
|
Depth Estimation (F16)
|
100% |
1610
12.5 IPS |
|
|
Depth Estimation (I8)
|
74% |
2225
17.3 IPS |
|
|
Style Transfer (F32)
|
100% |
4037
5.31 IPS |
|
|
Style Transfer (F16)
|
100% |
3768
4.96 IPS |
|
|
Style Transfer (I8)
|
85% |
3471
4.57 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
597
21.3 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
715
25.5 IPS |
|
|
Image Super-Resolution (I8)
|
97% |
709
25.3 IPS |
|
|
Text Classification (F32)
|
100% |
666
956.5 IPS |
|
|
Text Classification (F16)
|
100% |
684
983.2 IPS |
|
|
Text Classification (I8)
|
94% |
236
339.3 IPS |
|
|
Machine Translation (F32)
|
100% |
482
8.87 IPS |
|
|
Machine Translation (F16)
|
100% |
372
6.84 IPS |
|
|
Machine Translation (I8)
|
67% |
738
13.6 IPS |
