OPPO CPH2305
Geekbench ML Score
716
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 15 2024 01:41 PM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | OPPO CPH2305 |
| Model ID | OPPO CPH2305 |
| Motherboard | taro |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 2 part 3400 revision 0 |
| Base Frequency | 1.78 GHz |
| Cluster 1 | 4 Cores @ 1.79 GHz |
| Cluster 2 | 3 Cores @ 2.50 GHz |
| Cluster 3 | 1 Core @ 3.00 GHz |
| Memory Information | |
|---|---|
| Size | 10.95 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
461
86.3 IPS |
|
|
Image Classification (F16)
|
100% |
481
90.1 IPS |
|
|
Image Classification (I8)
|
97% |
441
82.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
684
11.4 IPS |
|
|
Image Segmentation (F16)
|
100% |
854
14.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
870
14.5 IPS |
|
|
Pose Estimation (F32)
|
100% |
692
0.84 IPS |
|
|
Pose Estimation (F16)
|
100% |
1266
1.53 IPS |
|
|
Pose Estimation (I8)
|
100% |
1262
1.53 IPS |
|
|
Object Detection (F32)
|
100% |
240
17.9 IPS |
|
|
Object Detection (F16)
|
100% |
341
25.4 IPS |
|
|
Object Detection (I8)
|
62% |
247
18.4 IPS |
|
|
Face Detection (F32)
|
100% |
1441
17.1 IPS |
|
|
Face Detection (F16)
|
97% |
1400
16.7 IPS |
|
|
Face Detection (I8)
|
87% |
1341
15.9 IPS |
|
|
Depth Estimation (F32)
|
100% |
1085
8.41 IPS |
|
|
Depth Estimation (F16)
|
100% |
2408
18.7 IPS |
|
|
Depth Estimation (I8)
|
95% |
2415
18.7 IPS |
|
|
Style Transfer (F32)
|
100% |
2690
3.54 IPS |
|
|
Style Transfer (F16)
|
100% |
5045
6.64 IPS |
|
|
Style Transfer (I8)
|
98% |
5066
6.66 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
319
11.4 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
425
15.2 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
519
18.5 IPS |
|
|
Text Classification (F32)
|
100% |
175
251.8 IPS |
|
|
Text Classification (F16)
|
100% |
182
261.4 IPS |
|
|
Text Classification (I8)
|
92% |
332
477.2 IPS |
|
|
Machine Translation (F32)
|
100% |
338
6.22 IPS |
|
|
Machine Translation (F16)
|
100% |
341
6.27 IPS |
|
|
Machine Translation (I8)
|
64% |
402
7.40 IPS |
