vivo V2309A
Geekbench ML Score
1142
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 04 2024 07:31 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | vivo V2309A |
| Model ID | vivo V2309A |
| 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 | 14.96 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
764
142.9 IPS |
|
|
Image Classification (F16)
|
100% |
850
159.1 IPS |
|
|
Image Classification (I8)
|
97% |
1206
225.6 IPS |
|
|
Image Segmentation (F32)
|
100% |
987
16.5 IPS |
|
|
Image Segmentation (F16)
|
100% |
806
13.5 IPS |
|
|
Image Segmentation (I8)
|
98% |
951
15.9 IPS |
|
|
Pose Estimation (F32)
|
100% |
1299
1.57 IPS |
|
|
Pose Estimation (F16)
|
100% |
1238
1.50 IPS |
|
|
Pose Estimation (I8)
|
100% |
5425
6.57 IPS |
|
|
Object Detection (F32)
|
100% |
510
38.1 IPS |
|
|
Object Detection (F16)
|
100% |
523
39.0 IPS |
|
|
Object Detection (I8)
|
61% |
764
57.1 IPS |
|
|
Face Detection (F32)
|
100% |
1512
18.0 IPS |
|
|
Face Detection (F16)
|
100% |
1488
17.7 IPS |
|
|
Face Detection (I8)
|
86% |
1617
19.2 IPS |
|
|
Depth Estimation (F32)
|
100% |
1240
9.62 IPS |
|
|
Depth Estimation (F16)
|
100% |
1273
9.87 IPS |
|
|
Depth Estimation (I8)
|
95% |
1558
12.1 IPS |
|
|
Style Transfer (F32)
|
100% |
2070
2.72 IPS |
|
|
Style Transfer (F16)
|
100% |
2058
2.71 IPS |
|
|
Style Transfer (I8)
|
98% |
3854
5.07 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
860
30.7 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
871
31.1 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1583
56.5 IPS |
|
|
Text Classification (F32)
|
100% |
803
1.15 KIPS |
|
|
Text Classification (F16)
|
100% |
899
1.29 KIPS |
|
|
Text Classification (I8)
|
92% |
694
997.1 IPS |
|
|
Machine Translation (F32)
|
100% |
1466
27.0 IPS |
|
|
Machine Translation (F16)
|
100% |
1383
25.4 IPS |
|
|
Machine Translation (I8)
|
64% |
295
5.44 IPS |
