OPPO PHY110
Geekbench ML Score
1008
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 26 2024 06:26 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | OPPO PHY110 |
| Model ID | OPPO PHY110 |
| Motherboard | pineapple |
| Governor | uag |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3458 revision 1 |
| Base Frequency | 2.26 GHz |
| Cluster 1 | 2 Cores @ 2.27 GHz |
| Cluster 2 | 2 Cores @ 2.96 GHz |
| Cluster 3 | 3 Cores @ 3.15 GHz |
| Cluster 4 | 1 Core @ 3.30 GHz |
| Memory Information | |
|---|---|
| Size | 14.90 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
493
92.2 IPS |
|
|
Image Classification (F16)
|
100% |
581
108.8 IPS |
|
|
Image Classification (I8)
|
99% |
549
102.7 IPS |
|
|
Image Segmentation (F32)
|
100% |
790
13.2 IPS |
|
|
Image Segmentation (F16)
|
100% |
1011
16.9 IPS |
|
|
Image Segmentation (I8)
|
98% |
956
16.0 IPS |
|
|
Pose Estimation (F32)
|
100% |
1063
1.29 IPS |
|
|
Pose Estimation (F16)
|
100% |
2488
3.01 IPS |
|
|
Pose Estimation (I8)
|
100% |
2468
2.99 IPS |
|
|
Object Detection (F32)
|
100% |
258
19.3 IPS |
|
|
Object Detection (F16)
|
100% |
273
20.3 IPS |
|
|
Object Detection (I8)
|
62% |
264
19.7 IPS |
|
|
Face Detection (F32)
|
100% |
2066
24.6 IPS |
|
|
Face Detection (F16)
|
97% |
3222
38.3 IPS |
|
|
Face Detection (I8)
|
86% |
1681
20.0 IPS |
|
|
Depth Estimation (F32)
|
100% |
1823
14.1 IPS |
|
|
Depth Estimation (F16)
|
100% |
3695
28.7 IPS |
|
|
Depth Estimation (I8)
|
95% |
3682
28.6 IPS |
|
|
Style Transfer (F32)
|
100% |
5205
6.85 IPS |
|
|
Style Transfer (F16)
|
100% |
7992
10.5 IPS |
|
|
Style Transfer (I8)
|
98% |
7728
10.2 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
411
14.7 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
598
21.3 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
628
22.4 IPS |
|
|
Text Classification (F32)
|
100% |
441
633.9 IPS |
|
|
Text Classification (F16)
|
100% |
414
594.3 IPS |
|
|
Text Classification (I8)
|
92% |
690
992.3 IPS |
|
|
Machine Translation (F32)
|
100% |
556
10.2 IPS |
|
|
Machine Translation (F16)
|
100% |
284
5.23 IPS |
|
|
Machine Translation (I8)
|
64% |
321
5.91 IPS |
