vivo V2339A
Geekbench ML Score
1230
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | June 09 2025 03:03 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | vivo V2339A |
| Model ID | vivo V2339A |
| 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 @ 3.25 GHz |
| Memory Information | |
|---|---|
| Size | 14.97 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
780
145.9 IPS |
|
|
Image Classification (F16)
|
100% |
804
150.4 IPS |
|
|
Image Classification (I8)
|
97% |
1135
212.4 IPS |
|
|
Image Segmentation (F32)
|
100% |
947
15.8 IPS |
|
|
Image Segmentation (F16)
|
100% |
994
16.6 IPS |
|
|
Image Segmentation (I8)
|
98% |
1250
20.9 IPS |
|
|
Pose Estimation (F32)
|
100% |
1274
1.54 IPS |
|
|
Pose Estimation (F16)
|
100% |
1214
1.47 IPS |
|
|
Pose Estimation (I8)
|
100% |
5540
6.71 IPS |
|
|
Object Detection (F32)
|
100% |
616
46.0 IPS |
|
|
Object Detection (F16)
|
100% |
635
47.4 IPS |
|
|
Object Detection (I8)
|
61% |
1194
89.1 IPS |
|
|
Face Detection (F32)
|
100% |
1582
18.8 IPS |
|
|
Face Detection (F16)
|
100% |
1525
18.1 IPS |
|
|
Face Detection (I8)
|
86% |
1720
20.5 IPS |
|
|
Depth Estimation (F32)
|
100% |
1407
10.9 IPS |
|
|
Depth Estimation (F16)
|
100% |
1358
10.5 IPS |
|
|
Depth Estimation (I8)
|
95% |
1927
14.9 IPS |
|
|
Style Transfer (F32)
|
100% |
2149
2.83 IPS |
|
|
Style Transfer (F16)
|
100% |
2067
2.72 IPS |
|
|
Style Transfer (I8)
|
98% |
4571
6.01 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
862
30.8 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
866
30.9 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1992
71.1 IPS |
|
|
Text Classification (F32)
|
100% |
790
1.14 KIPS |
|
|
Text Classification (F16)
|
100% |
746
1.07 KIPS |
|
|
Text Classification (I8)
|
92% |
566
813.2 IPS |
|
|
Machine Translation (F32)
|
100% |
1416
26.1 IPS |
|
|
Machine Translation (F16)
|
100% |
1395
25.7 IPS |
|
|
Machine Translation (I8)
|
64% |
500
9.19 IPS |
