samsung SM-F741B
Geekbench ML Score
1287
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 26 2024 01:05 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 | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
651
121.9 IPS |
|
|
Image Classification (F16)
|
100% |
653
122.3 IPS |
|
|
Image Classification (I8)
|
97% |
1091
204.1 IPS |
|
|
Image Segmentation (F32)
|
100% |
859
14.3 IPS |
|
|
Image Segmentation (F16)
|
100% |
854
14.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
976
16.3 IPS |
|
|
Pose Estimation (F32)
|
100% |
1497
1.81 IPS |
|
|
Pose Estimation (F16)
|
100% |
1481
1.79 IPS |
|
|
Pose Estimation (I8)
|
100% |
6183
7.49 IPS |
|
|
Object Detection (F32)
|
100% |
630
47.0 IPS |
|
|
Object Detection (F16)
|
100% |
635
47.4 IPS |
|
|
Object Detection (I8)
|
61% |
1420
106.0 IPS |
|
|
Face Detection (F32)
|
100% |
1519
18.1 IPS |
|
|
Face Detection (F16)
|
100% |
1498
17.8 IPS |
|
|
Face Detection (I8)
|
86% |
2692
32.0 IPS |
|
|
Depth Estimation (F32)
|
100% |
1473
11.4 IPS |
|
|
Depth Estimation (F16)
|
100% |
1428
11.1 IPS |
|
|
Depth Estimation (I8)
|
95% |
2803
21.7 IPS |
|
|
Style Transfer (F32)
|
100% |
2531
3.33 IPS |
|
|
Style Transfer (F16)
|
100% |
2488
3.27 IPS |
|
|
Style Transfer (I8)
|
98% |
5197
6.84 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
1024
36.6 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1008
36.0 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2451
87.5 IPS |
|
|
Text Classification (F32)
|
100% |
602
864.9 IPS |
|
|
Text Classification (F16)
|
100% |
631
907.3 IPS |
|
|
Text Classification (I8)
|
92% |
531
762.5 IPS |
|
|
Machine Translation (F32)
|
100% |
1483
27.3 IPS |
|
|
Machine Translation (F16)
|
100% |
1418
26.1 IPS |
|
|
Machine Translation (I8)
|
64% |
599
11.0 IPS |
