LENOVO S5030
Geekbench AI Score
583
Single Precision Score
341
Half Precision Score
580
Quantized Score
Geekbench AI 1.2.0 for Windows AVX2
Result Information
| Upload Date | February 01 2025 10:50 PM |
| Views | 2 |
System Information
| AI Information | |
|---|---|
| Framework | ONNX |
| Backend | CPU |
| Device | Intel Core i5-5200U |
| System Information | |
|---|---|
| Operating System | Windows 10 Pro (64-bit) |
| Model | LENOVO S5030 |
| Motherboard | LENOVO CRESCENTBAY |
| CPU Information | |
|---|---|
| Name | Intel Core i5-5200U |
| Topology | 1 Processor, 2 Cores, 4 Threads |
| Identifier | GenuineIntel Family 6 Model 61 Stepping 4 |
| Base Frequency | 2.20 GHz |
| Cluster 1 | 2 Cores |
| Memory Information | |
|---|---|
| Size | 8.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
396
73.6 IPS |
|
|
Image Classification (HP)
|
100% |
161
30.0 IPS |
|
|
Image Classification (Q)
|
96% |
456
85.2 IPS |
|
|
Image Segmentation (SP)
|
100% |
209
3.40 IPS |
|
|
Image Segmentation (HP)
|
100% |
166
2.69 IPS |
|
|
Image Segmentation (Q)
|
97% |
275
4.47 IPS |
|
|
Pose Estimation (SP)
|
100% |
875
1.02 IPS |
|
|
Pose Estimation (HP)
|
100% |
733
0.85 IPS |
|
|
Pose Estimation (Q)
|
86% |
970
1.15 IPS |
|
|
Object Detection (SP)
|
100% |
392
31.1 IPS |
|
|
Object Detection (HP)
|
100% |
189
15.0 IPS |
|
|
Object Detection (Q)
|
58% |
311
34.0 IPS |
|
|
Face Detection (SP)
|
100% |
780
9.27 IPS |
|
|
Face Detection (HP)
|
100% |
379
4.51 IPS |
|
|
Face Detection (Q)
|
96% |
1096
13.1 IPS |
|
|
Depth Estimation (SP)
|
100% |
1073
8.27 IPS |
|
|
Depth Estimation (HP)
|
99% |
507
3.91 IPS |
|
|
Depth Estimation (Q)
|
78% |
1118
8.93 IPS |
|
|
Style Transfer (SP)
|
100% |
2044
2.63 IPS |
|
|
Style Transfer (HP)
|
100% |
1663
2.14 IPS |
|
|
Style Transfer (Q)
|
87% |
2035
2.65 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
444
16.4 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
373
13.8 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
462
17.1 IPS |
|
|
Text Classification (SP)
|
100% |
415
553.5 IPS |
|
|
Text Classification (HP)
|
100% |
230
307.4 IPS |
|
|
Text Classification (Q)
|
97% |
206
275.9 IPS |
|
|
Machine Translation (SP)
|
100% |
514
8.85 IPS |
|
|
Machine Translation (HP)
|
100% |
213
3.67 IPS |
|
|
Machine Translation (Q)
|
68% |
487
9.40 IPS |
