vivo V2352A
Geekbench ML Score
311
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 23 2024 01:45 AM |
| Views | 3 |
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 | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
170
31.9 IPS |
|
|
Image Classification (F16)
|
100% |
133
24.9 IPS |
|
|
Image Classification (I8)
|
97% |
341
63.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
172
2.88 IPS |
|
|
Image Segmentation (F16)
|
100% |
172
2.88 IPS |
|
|
Image Segmentation (I8)
|
98% |
361
6.03 IPS |
|
|
Pose Estimation (F32)
|
100% |
224
0.27 IPS |
|
|
Pose Estimation (F16)
|
100% |
229
0.28 IPS |
|
|
Pose Estimation (I8)
|
100% |
882
1.07 IPS |
|
|
Object Detection (F32)
|
100% |
137
10.2 IPS |
|
|
Object Detection (F16)
|
100% |
137
10.2 IPS |
|
|
Object Detection (I8)
|
61% |
376
28.1 IPS |
|
|
Face Detection (F32)
|
100% |
377
4.48 IPS |
|
|
Face Detection (F16)
|
100% |
376
4.48 IPS |
|
|
Face Detection (I8)
|
86% |
780
9.27 IPS |
|
|
Depth Estimation (F32)
|
100% |
313
2.43 IPS |
|
|
Depth Estimation (F16)
|
100% |
314
2.43 IPS |
|
|
Depth Estimation (I8)
|
95% |
863
6.69 IPS |
|
|
Style Transfer (F32)
|
100% |
567
0.75 IPS |
|
|
Style Transfer (F16)
|
100% |
567
0.75 IPS |
|
|
Style Transfer (I8)
|
98% |
1601
2.11 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
157
5.59 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
157
5.60 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
543
19.4 IPS |
|
|
Text Classification (F32)
|
100% |
175
251.3 IPS |
|
|
Text Classification (F16)
|
100% |
175
250.9 IPS |
|
|
Text Classification (I8)
|
92% |
296
425.8 IPS |
|
|
Machine Translation (F32)
|
100% |
334
6.15 IPS |
|
|
Machine Translation (F16)
|
100% |
334
6.15 IPS |
|
|
Machine Translation (I8)
|
64% |
381
7.02 IPS |
