vivo V2352GA
Geekbench ML Score
454
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | April 05 2025 03:55 PM |
| Views | 8 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | vivo V2352GA |
| Model ID | vivo V2352GA |
| 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 | 14.94 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
200
37.5 IPS |
|
|
Image Classification (F16)
|
100% |
200
37.4 IPS |
|
|
Image Classification (I8)
|
97% |
508
95.1 IPS |
|
|
Image Segmentation (F32)
|
100% |
258
4.30 IPS |
|
|
Image Segmentation (F16)
|
100% |
256
4.27 IPS |
|
|
Image Segmentation (I8)
|
98% |
522
8.72 IPS |
|
|
Pose Estimation (F32)
|
100% |
359
0.43 IPS |
|
|
Pose Estimation (F16)
|
100% |
363
0.44 IPS |
|
|
Pose Estimation (I8)
|
100% |
1373
1.66 IPS |
|
|
Object Detection (F32)
|
100% |
208
15.5 IPS |
|
|
Object Detection (F16)
|
100% |
209
15.6 IPS |
|
|
Object Detection (I8)
|
61% |
555
41.4 IPS |
|
|
Face Detection (F32)
|
100% |
515
6.12 IPS |
|
|
Face Detection (F16)
|
100% |
513
6.09 IPS |
|
|
Face Detection (I8)
|
86% |
1146
13.6 IPS |
|
|
Depth Estimation (F32)
|
100% |
452
3.51 IPS |
|
|
Depth Estimation (F16)
|
100% |
453
3.51 IPS |
|
|
Depth Estimation (I8)
|
95% |
1254
9.72 IPS |
|
|
Style Transfer (F32)
|
100% |
750
0.99 IPS |
|
|
Style Transfer (F16)
|
100% |
749
0.99 IPS |
|
|
Style Transfer (I8)
|
98% |
1796
2.36 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
236
8.43 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
235
8.40 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
835
29.8 IPS |
|
|
Text Classification (F32)
|
100% |
275
395.1 IPS |
|
|
Text Classification (F16)
|
100% |
278
398.9 IPS |
|
|
Text Classification (I8)
|
92% |
467
671.4 IPS |
|
|
Machine Translation (F32)
|
100% |
485
8.93 IPS |
|
|
Machine Translation (F16)
|
100% |
488
8.97 IPS |
|
|
Machine Translation (I8)
|
64% |
556
10.2 IPS |
