samsung SM-F741B
Geekbench ML Score
1158
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 26 2024 01:08 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-F741B |
| Model ID | samsung SM-F741B |
| 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% |
395
74.0 IPS |
|
|
Image Classification (F16)
|
100% |
493
92.2 IPS |
|
|
Image Classification (I8)
|
99% |
712
133.3 IPS |
|
|
Image Segmentation (F32)
|
100% |
943
15.7 IPS |
|
|
Image Segmentation (F16)
|
100% |
1381
23.1 IPS |
|
|
Image Segmentation (I8)
|
98% |
1390
23.2 IPS |
|
|
Pose Estimation (F32)
|
100% |
1088
1.32 IPS |
|
|
Pose Estimation (F16)
|
100% |
2308
2.79 IPS |
|
|
Pose Estimation (I8)
|
100% |
2318
2.81 IPS |
|
|
Object Detection (F32)
|
100% |
373
27.8 IPS |
|
|
Object Detection (F16)
|
100% |
261
19.5 IPS |
|
|
Object Detection (I8)
|
62% |
373
27.8 IPS |
|
|
Face Detection (F32)
|
100% |
2020
24.0 IPS |
|
|
Face Detection (F16)
|
97% |
1754
20.9 IPS |
|
|
Face Detection (I8)
|
86% |
1766
21.0 IPS |
|
|
Depth Estimation (F32)
|
100% |
1723
13.4 IPS |
|
|
Depth Estimation (F16)
|
100% |
3590
27.8 IPS |
|
|
Depth Estimation (I8)
|
95% |
3572
27.7 IPS |
|
|
Style Transfer (F32)
|
100% |
6433
8.46 IPS |
|
|
Style Transfer (F16)
|
100% |
10234
13.5 IPS |
|
|
Style Transfer (I8)
|
98% |
10066
13.2 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
458
16.4 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
724
25.8 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
804
28.7 IPS |
|
|
Text Classification (F32)
|
100% |
455
653.3 IPS |
|
|
Text Classification (F16)
|
100% |
448
644.1 IPS |
|
|
Text Classification (I8)
|
92% |
733
1.05 KIPS |
|
|
Machine Translation (F32)
|
100% |
732
13.5 IPS |
|
|
Machine Translation (F16)
|
100% |
712
13.1 IPS |
|
|
Machine Translation (I8)
|
64% |
824
15.2 IPS |
