samsung SM-F956B
Geekbench ML Score
1184
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 15 2024 12:34 PM |
| Views | 3 |
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 | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
379
70.9 IPS |
|
|
Image Classification (F16)
|
100% |
743
139.1 IPS |
|
|
Image Classification (I8)
|
99% |
486
90.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
790
13.2 IPS |
|
|
Image Segmentation (F16)
|
100% |
1000
16.7 IPS |
|
|
Image Segmentation (I8)
|
98% |
1014
16.9 IPS |
|
|
Pose Estimation (F32)
|
100% |
1084
1.31 IPS |
|
|
Pose Estimation (F16)
|
100% |
2669
3.23 IPS |
|
|
Pose Estimation (I8)
|
100% |
2658
3.22 IPS |
|
|
Object Detection (F32)
|
100% |
332
24.8 IPS |
|
|
Object Detection (F16)
|
100% |
411
30.7 IPS |
|
|
Object Detection (I8)
|
62% |
265
19.8 IPS |
|
|
Face Detection (F32)
|
100% |
1759
20.9 IPS |
|
|
Face Detection (F16)
|
97% |
2385
28.4 IPS |
|
|
Face Detection (I8)
|
86% |
2174
25.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
1894
14.7 IPS |
|
|
Depth Estimation (F16)
|
100% |
4017
31.2 IPS |
|
|
Depth Estimation (I8)
|
95% |
3969
30.8 IPS |
|
|
Style Transfer (F32)
|
100% |
7236
9.52 IPS |
|
|
Style Transfer (F16)
|
100% |
10756
14.1 IPS |
|
|
Style Transfer (I8)
|
98% |
10730
14.1 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
452
16.1 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1002
35.8 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
874
31.2 IPS |
|
|
Text Classification (F32)
|
100% |
446
640.6 IPS |
|
|
Text Classification (F16)
|
100% |
442
634.9 IPS |
|
|
Text Classification (I8)
|
92% |
723
1.04 KIPS |
|
|
Machine Translation (F32)
|
100% |
723
13.3 IPS |
|
|
Machine Translation (F16)
|
100% |
694
12.8 IPS |
|
|
Machine Translation (I8)
|
64% |
807
14.9 IPS |
