samsung SM-S9260
Geekbench ML Score
1291
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | April 25 2024 01:04 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-S9260 |
| Model ID | samsung SM-S9260 |
| Motherboard | pineapple |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3458 revision 1 |
| Base Frequency | 2.26 GHz |
| Cluster 1 | 2 Cores @ 2.27 GHz |
| Cluster 2 | 2 Cores @ 2.96 GHz |
| Cluster 3 | 3 Cores @ 3.15 GHz |
| Cluster 4 | 1 Core @ 3.40 GHz |
| Memory Information | |
|---|---|
| Size | 10.81 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
625
116.9 IPS |
|
|
Image Classification (F16)
|
100% |
615
115.1 IPS |
|
|
Image Classification (I8)
|
97% |
1088
203.6 IPS |
|
|
Image Segmentation (F32)
|
100% |
830
13.9 IPS |
|
|
Image Segmentation (F16)
|
100% |
837
14.0 IPS |
|
|
Image Segmentation (I8)
|
98% |
1014
16.9 IPS |
|
|
Pose Estimation (F32)
|
100% |
1441
1.74 IPS |
|
|
Pose Estimation (F16)
|
100% |
1361
1.65 IPS |
|
|
Pose Estimation (I8)
|
100% |
6330
7.67 IPS |
|
|
Object Detection (F32)
|
100% |
632
47.1 IPS |
|
|
Object Detection (F16)
|
100% |
643
48.0 IPS |
|
|
Object Detection (I8)
|
61% |
1381
103.1 IPS |
|
|
Face Detection (F32)
|
100% |
1450
17.2 IPS |
|
|
Face Detection (F16)
|
100% |
1433
17.0 IPS |
|
|
Face Detection (I8)
|
86% |
2656
31.6 IPS |
|
|
Depth Estimation (F32)
|
100% |
1389
10.8 IPS |
|
|
Depth Estimation (F16)
|
100% |
1417
11.0 IPS |
|
|
Depth Estimation (I8)
|
95% |
2790
21.6 IPS |
|
|
Style Transfer (F32)
|
100% |
2467
3.24 IPS |
|
|
Style Transfer (F16)
|
100% |
2477
3.26 IPS |
|
|
Style Transfer (I8)
|
98% |
5435
7.15 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
978
34.9 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1041
37.2 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2937
104.9 IPS |
|
|
Text Classification (F32)
|
100% |
773
1.11 KIPS |
|
|
Text Classification (F16)
|
100% |
687
987.8 IPS |
|
|
Text Classification (I8)
|
92% |
501
719.8 IPS |
|
|
Machine Translation (F32)
|
100% |
1535
28.3 IPS |
|
|
Machine Translation (F16)
|
100% |
1452
26.7 IPS |
|
|
Machine Translation (I8)
|
64% |
590
10.9 IPS |
