vivo V2352A
Geekbench ML Score
641
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 23 2024 01:35 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | vivo V2352A |
| Model ID | vivo V2352A |
| 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 | 2.02 GHz |
| Cluster 1 | 3 Cores @ 2.02 GHz |
| Cluster 2 | 4 Cores @ 2.80 GHz |
| Cluster 3 | 1 Core @ 3.01 GHz |
| Memory Information | |
|---|---|
| Size | 11.03 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
375
70.2 IPS |
|
|
Image Classification (F16)
|
100% |
644
120.4 IPS |
|
|
Image Classification (I8)
|
99% |
624
116.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
483
8.07 IPS |
|
|
Image Segmentation (F16)
|
100% |
974
16.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
1081
18.0 IPS |
|
|
Pose Estimation (F32)
|
100% |
624
0.76 IPS |
|
|
Pose Estimation (F16)
|
100% |
976
1.18 IPS |
|
|
Pose Estimation (I8)
|
100% |
970
1.17 IPS |
|
|
Object Detection (F32)
|
100% |
217
16.2 IPS |
|
|
Object Detection (F16)
|
100% |
222
16.6 IPS |
|
|
Object Detection (I8)
|
62% |
266
19.9 IPS |
|
|
Face Detection (F32)
|
100% |
1103
13.1 IPS |
|
|
Face Detection (F16)
|
97% |
1688
20.1 IPS |
|
|
Face Detection (I8)
|
86% |
1407
16.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
825
6.40 IPS |
|
|
Depth Estimation (F16)
|
100% |
1335
10.4 IPS |
|
|
Depth Estimation (I8)
|
95% |
1312
10.2 IPS |
|
|
Style Transfer (F32)
|
100% |
2318
3.05 IPS |
|
|
Style Transfer (F16)
|
100% |
3816
5.02 IPS |
|
|
Style Transfer (I8)
|
98% |
3432
4.51 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
294
10.5 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
473
16.9 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
469
16.7 IPS |
|
|
Text Classification (F32)
|
100% |
175
251.4 IPS |
|
|
Text Classification (F16)
|
100% |
175
251.0 IPS |
|
|
Text Classification (I8)
|
92% |
297
427.0 IPS |
|
|
Machine Translation (F32)
|
100% |
334
6.15 IPS |
|
|
Machine Translation (F16)
|
100% |
333
6.13 IPS |
|
|
Machine Translation (I8)
|
64% |
381
7.01 IPS |
