OPPO PHY110
Geekbench ML Score
276
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 26 2024 08:39 AM |
| Views | 3 |
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 | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
226
42.3 IPS |
|
|
Image Classification (F16)
|
100% |
231
43.2 IPS |
|
|
Image Classification (I8)
|
97% |
285
53.3 IPS |
|
|
Image Segmentation (F32)
|
100% |
149
2.48 IPS |
|
|
Image Segmentation (F16)
|
100% |
149
2.49 IPS |
|
|
Image Segmentation (I8)
|
98% |
299
4.99 IPS |
|
|
Pose Estimation (F32)
|
100% |
216
0.26 IPS |
|
|
Pose Estimation (F16)
|
100% |
194
0.23 IPS |
|
|
Pose Estimation (I8)
|
100% |
725
0.88 IPS |
|
|
Object Detection (F32)
|
100% |
119
8.90 IPS |
|
|
Object Detection (F16)
|
100% |
120
8.93 IPS |
|
|
Object Detection (I8)
|
61% |
321
24.0 IPS |
|
|
Face Detection (F32)
|
100% |
345
4.10 IPS |
|
|
Face Detection (F16)
|
100% |
342
4.07 IPS |
|
|
Face Detection (I8)
|
86% |
658
7.82 IPS |
|
|
Depth Estimation (F32)
|
100% |
269
2.09 IPS |
|
|
Depth Estimation (F16)
|
100% |
269
2.09 IPS |
|
|
Depth Estimation (I8)
|
95% |
724
5.62 IPS |
|
|
Style Transfer (F32)
|
100% |
478
0.63 IPS |
|
|
Style Transfer (F16)
|
100% |
478
0.63 IPS |
|
|
Style Transfer (I8)
|
98% |
1333
1.75 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
133
4.73 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
132
4.73 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
453
16.2 IPS |
|
|
Text Classification (F32)
|
100% |
146
209.8 IPS |
|
|
Text Classification (F16)
|
100% |
146
210.1 IPS |
|
|
Text Classification (I8)
|
92% |
239
344.0 IPS |
|
|
Machine Translation (F32)
|
100% |
284
5.22 IPS |
|
|
Machine Translation (F16)
|
100% |
284
5.23 IPS |
|
|
Machine Translation (I8)
|
64% |
321
5.90 IPS |
