samsung SM-F956B
Geekbench ML Score
1084
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 24 2024 05:14 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-F956B |
| Model ID | samsung SM-F956B |
| 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 | 10.86 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
623
116.6 IPS |
|
|
Image Classification (F16)
|
100% |
615
115.1 IPS |
|
|
Image Classification (I8)
|
97% |
991
185.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
684
11.4 IPS |
|
|
Image Segmentation (F16)
|
100% |
679
11.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
780
13.0 IPS |
|
|
Pose Estimation (F32)
|
100% |
1406
1.70 IPS |
|
|
Pose Estimation (F16)
|
100% |
1314
1.59 IPS |
|
|
Pose Estimation (I8)
|
100% |
5667
6.86 IPS |
|
|
Object Detection (F32)
|
100% |
536
40.0 IPS |
|
|
Object Detection (F16)
|
100% |
538
40.2 IPS |
|
|
Object Detection (I8)
|
61% |
1112
83.0 IPS |
|
|
Face Detection (F32)
|
100% |
1256
14.9 IPS |
|
|
Face Detection (F16)
|
100% |
1241
14.8 IPS |
|
|
Face Detection (I8)
|
86% |
2265
26.9 IPS |
|
|
Depth Estimation (F32)
|
100% |
1258
9.75 IPS |
|
|
Depth Estimation (F16)
|
100% |
1267
9.82 IPS |
|
|
Depth Estimation (I8)
|
95% |
2212
17.2 IPS |
|
|
Style Transfer (F32)
|
100% |
2425
3.19 IPS |
|
|
Style Transfer (F16)
|
100% |
2333
3.07 IPS |
|
|
Style Transfer (I8)
|
98% |
4364
5.74 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
713
25.5 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
712
25.4 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1705
60.9 IPS |
|
|
Text Classification (F32)
|
100% |
483
693.5 IPS |
|
|
Text Classification (F16)
|
100% |
500
718.5 IPS |
|
|
Text Classification (I8)
|
92% |
356
512.0 IPS |
|
|
Machine Translation (F32)
|
100% |
1426
26.2 IPS |
|
|
Machine Translation (F16)
|
100% |
1406
25.9 IPS |
|
|
Machine Translation (I8)
|
64% |
545
10.0 IPS |
