vivo V2324HA
Geekbench ML Score
1302
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 26 2024 02:30 AM |
| Views | 3 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | vivo V2324HA |
| Model ID | vivo V2324HA |
| 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 | 14.95 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
873
163.3 IPS |
|
|
Image Classification (F16)
|
100% |
886
165.8 IPS |
|
|
Image Classification (I8)
|
97% |
1405
262.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
955
15.9 IPS |
|
|
Image Segmentation (F16)
|
100% |
918
15.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
1040
17.4 IPS |
|
|
Pose Estimation (F32)
|
100% |
1420
1.72 IPS |
|
|
Pose Estimation (F16)
|
100% |
1396
1.69 IPS |
|
|
Pose Estimation (I8)
|
100% |
6137
7.43 IPS |
|
|
Object Detection (F32)
|
100% |
635
47.4 IPS |
|
|
Object Detection (F16)
|
100% |
666
49.7 IPS |
|
|
Object Detection (I8)
|
61% |
994
74.2 IPS |
|
|
Face Detection (F32)
|
100% |
1550
18.4 IPS |
|
|
Face Detection (F16)
|
100% |
1540
18.3 IPS |
|
|
Face Detection (I8)
|
86% |
1814
21.6 IPS |
|
|
Depth Estimation (F32)
|
100% |
1416
11.0 IPS |
|
|
Depth Estimation (F16)
|
100% |
1446
11.2 IPS |
|
|
Depth Estimation (I8)
|
95% |
1872
14.5 IPS |
|
|
Style Transfer (F32)
|
100% |
2179
2.87 IPS |
|
|
Style Transfer (F16)
|
100% |
2206
2.90 IPS |
|
|
Style Transfer (I8)
|
98% |
4651
6.12 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
957
34.2 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
977
34.9 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2434
86.9 IPS |
|
|
Text Classification (F32)
|
100% |
884
1.27 KIPS |
|
|
Text Classification (F16)
|
100% |
934
1.34 KIPS |
|
|
Text Classification (I8)
|
92% |
710
1.02 KIPS |
|
|
Machine Translation (F32)
|
100% |
1594
29.3 IPS |
|
|
Machine Translation (F16)
|
100% |
1541
28.4 IPS |
|
|
Machine Translation (I8)
|
64% |
451
8.31 IPS |
