OPPO PHY110
Geekbench ML Score
1236
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 26 2024 02:24 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 | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
577
107.9 IPS |
|
|
Image Classification (F16)
|
100% |
572
107.1 IPS |
|
|
Image Classification (I8)
|
97% |
989
185.1 IPS |
|
|
Image Segmentation (F32)
|
100% |
745
12.4 IPS |
|
|
Image Segmentation (F16)
|
100% |
750
12.5 IPS |
|
|
Image Segmentation (I8)
|
98% |
692
11.6 IPS |
|
|
Pose Estimation (F32)
|
100% |
1523
1.84 IPS |
|
|
Pose Estimation (F16)
|
100% |
1525
1.85 IPS |
|
|
Pose Estimation (I8)
|
100% |
6057
7.34 IPS |
|
|
Object Detection (F32)
|
100% |
620
46.3 IPS |
|
|
Object Detection (F16)
|
100% |
627
46.8 IPS |
|
|
Object Detection (I8)
|
61% |
1463
109.2 IPS |
|
|
Face Detection (F32)
|
100% |
1387
16.5 IPS |
|
|
Face Detection (F16)
|
100% |
1406
16.7 IPS |
|
|
Face Detection (I8)
|
86% |
2338
27.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
1473
11.4 IPS |
|
|
Depth Estimation (F16)
|
100% |
1440
11.2 IPS |
|
|
Depth Estimation (I8)
|
95% |
2439
18.9 IPS |
|
|
Style Transfer (F32)
|
100% |
2653
3.49 IPS |
|
|
Style Transfer (F16)
|
100% |
2687
3.53 IPS |
|
|
Style Transfer (I8)
|
98% |
5693
7.49 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
987
35.2 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
990
35.4 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2327
83.1 IPS |
|
|
Text Classification (F32)
|
100% |
715
1.03 KIPS |
|
|
Text Classification (F16)
|
100% |
733
1.05 KIPS |
|
|
Text Classification (I8)
|
92% |
489
702.9 IPS |
|
|
Machine Translation (F32)
|
100% |
1493
27.5 IPS |
|
|
Machine Translation (F16)
|
100% |
1491
27.4 IPS |
|
|
Machine Translation (I8)
|
64% |
468
8.61 IPS |
