vivo V2339FA
Geekbench ML Score
1376
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | March 30 2025 01:54 PM |
| Views | 10 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | vivo V2339FA |
| Model ID | vivo V2339FA |
| Motherboard | k6989v1_64 |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3458 revision 1 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 4 Cores @ 2.00 GHz |
| Cluster 2 | 3 Cores @ 2.85 GHz |
| Cluster 3 | 1 Core @ 3.40 GHz |
| Memory Information | |
|---|---|
| Size | 11.06 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
818
153.1 IPS |
|
|
Image Classification (F16)
|
100% |
827
154.7 IPS |
|
|
Image Classification (I8)
|
97% |
1239
231.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
1095
18.3 IPS |
|
|
Image Segmentation (F16)
|
100% |
1090
18.2 IPS |
|
|
Image Segmentation (I8)
|
98% |
1116
18.6 IPS |
|
|
Pose Estimation (F32)
|
100% |
1439
1.74 IPS |
|
|
Pose Estimation (F16)
|
100% |
1412
1.71 IPS |
|
|
Pose Estimation (I8)
|
100% |
6287
7.61 IPS |
|
|
Object Detection (F32)
|
100% |
815
60.8 IPS |
|
|
Object Detection (F16)
|
100% |
783
58.5 IPS |
|
|
Object Detection (I8)
|
61% |
1193
89.1 IPS |
|
|
Face Detection (F32)
|
100% |
1740
20.7 IPS |
|
|
Face Detection (F16)
|
100% |
1695
20.2 IPS |
|
|
Face Detection (I8)
|
86% |
2102
25.0 IPS |
|
|
Depth Estimation (F32)
|
100% |
1563
12.1 IPS |
|
|
Depth Estimation (F16)
|
100% |
1518
11.8 IPS |
|
|
Depth Estimation (I8)
|
95% |
2158
16.7 IPS |
|
|
Style Transfer (F32)
|
100% |
2333
3.07 IPS |
|
|
Style Transfer (F16)
|
100% |
2341
3.08 IPS |
|
|
Style Transfer (I8)
|
98% |
5008
6.59 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
996
35.6 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1004
35.8 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
3039
108.5 IPS |
|
|
Text Classification (F32)
|
100% |
880
1.26 KIPS |
|
|
Text Classification (F16)
|
100% |
806
1.16 KIPS |
|
|
Text Classification (I8)
|
92% |
480
690.2 IPS |
|
|
Machine Translation (F32)
|
100% |
1651
30.4 IPS |
|
|
Machine Translation (F16)
|
100% |
1518
27.9 IPS |
|
|
Machine Translation (I8)
|
64% |
598
11.0 IPS |
