samsung SM-A055F
Geekbench ML Score
125
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 06 2024 12:15 PM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-A055F |
| Model ID | samsung SM-A055F |
| Motherboard | a05m |
| CPU Information | |
|---|---|
| Name | ARM MT6769V/CZ |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 3 part 3338 revision 1 |
| Base Frequency | 1.80 GHz |
| Cluster 1 | 6 Cores @ 1.80 GHz |
| Cluster 2 | 2 Cores @ 2.00 GHz |
| Memory Information | |
|---|---|
| Size | 3.59 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
110
20.6 IPS |
|
|
Image Classification (F16)
|
100% |
59
11.0 IPS |
|
|
Image Classification (I8)
|
97% |
90
16.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
114
1.90 IPS |
|
|
Image Segmentation (F16)
|
100% |
106
1.77 IPS |
|
|
Image Segmentation (I8)
|
98% |
159
2.66 IPS |
|
|
Pose Estimation (F32)
|
100% |
151
0.18 IPS |
|
|
Pose Estimation (F16)
|
100% |
152
0.18 IPS |
|
|
Pose Estimation (I8)
|
100% |
670
0.81 IPS |
|
|
Object Detection (F32)
|
100% |
63
4.71 IPS |
|
|
Object Detection (F16)
|
100% |
70
5.22 IPS |
|
|
Object Detection (I8)
|
61% |
120
8.99 IPS |
|
|
Face Detection (F32)
|
100% |
188
2.23 IPS |
|
|
Face Detection (F16)
|
100% |
180
2.14 IPS |
|
|
Face Detection (I8)
|
86% |
299
3.56 IPS |
|
|
Depth Estimation (F32)
|
100% |
161
1.25 IPS |
|
|
Depth Estimation (F16)
|
100% |
147
1.14 IPS |
|
|
Depth Estimation (I8)
|
95% |
311
2.41 IPS |
|
|
Style Transfer (F32)
|
100% |
276
0.36 IPS |
|
|
Style Transfer (F16)
|
100% |
331
0.44 IPS |
|
|
Style Transfer (I8)
|
98% |
840
1.11 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
85
3.02 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
97
3.46 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
242
8.65 IPS |
|
|
Text Classification (F32)
|
100% |
31
45.2 IPS |
|
|
Text Classification (F16)
|
100% |
28
40.6 IPS |
|
|
Text Classification (I8)
|
92% |
25
35.9 IPS |
|
|
Machine Translation (F32)
|
100% |
72
1.32 IPS |
|
|
Machine Translation (F16)
|
100% |
80
1.47 IPS |
|
|
Machine Translation (I8)
|
64% |
77
1.41 IPS |
