[2015] Mini PC: Giada / Shenzhen Jiehe [F200] To be filled by O.E.M. To be filled by O.E.M.
Geekbench AI Score
91
Single Precision Score
52
Half Precision Score
112
Quantized Score
Geekbench AI 1.2.0 for Windows x86 (64-bit)
Result Information
| User | inc7007 |
| Upload Date | May 07 2025 05:20 PM |
| Views | 4 |
System Information
| AI Information | |
|---|---|
| Framework | ONNX |
| Backend | CPU |
| Device | Intel Celeron N2807 |
| System Information | |
|---|---|
| Operating System | Microsoft Windows 10 Pro (64-bit) |
| Model | To be filled by O.E.M. To be filled by O.E.M. |
| Motherboard | GIADA BayTrail |
| Power Plan | Balanced |
| CPU Information | |
|---|---|
| Name | Intel Celeron N2807 |
| Topology | 1 Processor, 2 Cores |
| Identifier | GenuineIntel Family 6 Model 55 Stepping 8 |
| Base Frequency | 1.58 GHz |
| Cluster 1 | 2 Cores |
| Memory Information | |
|---|---|
| Size | 2.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
57
10.6 IPS |
|
|
Image Classification (HP)
|
100% |
27
5.01 IPS |
|
|
Image Classification (Q)
|
96% |
81
15.1 IPS |
|
|
Image Segmentation (SP)
|
100% |
52
0.84 IPS |
|
|
Image Segmentation (HP)
|
100% |
38
0.61 IPS |
|
|
Image Segmentation (Q)
|
97% |
67
1.09 IPS |
|
|
Pose Estimation (SP)
|
100% |
107
0.12 IPS |
|
|
Pose Estimation (HP)
|
100% |
67
0.08 IPS |
|
|
Pose Estimation (Q)
|
86% |
154
0.18 IPS |
|
|
Object Detection (SP)
|
100% |
55
4.34 IPS |
|
|
Object Detection (HP)
|
100% |
29
2.27 IPS |
|
|
Object Detection (Q)
|
58% |
58
6.29 IPS |
|
|
Face Detection (SP)
|
100% |
163
1.94 IPS |
|
|
Face Detection (HP)
|
100% |
83
0.99 IPS |
|
|
Face Detection (Q)
|
96% |
196
2.34 IPS |
|
|
Depth Estimation (SP)
|
100% |
145
1.12 IPS |
|
|
Depth Estimation (HP)
|
99% |
76
0.59 IPS |
|
|
Depth Estimation (Q)
|
78% |
179
1.43 IPS |
|
|
Style Transfer (SP)
|
100% |
269
0.35 IPS |
|
|
Style Transfer (HP)
|
100% |
194
0.25 IPS |
|
|
Style Transfer (Q)
|
87% |
349
0.45 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
63
2.31 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
41
1.52 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
81
3.01 IPS |
|
|
Text Classification (SP)
|
100% |
51
68.0 IPS |
|
|
Text Classification (HP)
|
100% |
37
49.4 IPS |
|
|
Text Classification (Q)
|
97% |
53
71.3 IPS |
|
|
Machine Translation (SP)
|
100% |
114
1.96 IPS |
|
|
Machine Translation (HP)
|
100% |
44
0.75 IPS |
|
|
Machine Translation (Q)
|
68% |
126
2.44 IPS |
