OPPO CPH2305
Geekbench ML Score
680
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 15 2024 01:44 PM |
| Views | 1 |
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 | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
341
63.9 IPS |
|
|
Image Classification (F16)
|
100% |
342
64.0 IPS |
|
|
Image Classification (I8)
|
97% |
511
95.7 IPS |
|
|
Image Segmentation (F32)
|
100% |
448
7.48 IPS |
|
|
Image Segmentation (F16)
|
100% |
454
7.58 IPS |
|
|
Image Segmentation (I8)
|
98% |
511
8.53 IPS |
|
|
Pose Estimation (F32)
|
100% |
818
0.99 IPS |
|
|
Pose Estimation (F16)
|
100% |
785
0.95 IPS |
|
|
Pose Estimation (I8)
|
100% |
3027
3.67 IPS |
|
|
Object Detection (F32)
|
100% |
341
25.4 IPS |
|
|
Object Detection (F16)
|
100% |
355
26.5 IPS |
|
|
Object Detection (I8)
|
61% |
715
53.4 IPS |
|
|
Face Detection (F32)
|
100% |
760
9.03 IPS |
|
|
Face Detection (F16)
|
100% |
757
9.00 IPS |
|
|
Face Detection (I8)
|
86% |
1293
15.4 IPS |
|
|
Depth Estimation (F32)
|
100% |
830
6.43 IPS |
|
|
Depth Estimation (F16)
|
100% |
815
6.32 IPS |
|
|
Depth Estimation (I8)
|
95% |
1648
12.8 IPS |
|
|
Style Transfer (F32)
|
100% |
1459
1.92 IPS |
|
|
Style Transfer (F16)
|
100% |
1476
1.94 IPS |
|
|
Style Transfer (I8)
|
98% |
2753
3.62 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
469
16.7 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
483
17.3 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1162
41.5 IPS |
|
|
Text Classification (F32)
|
100% |
385
553.1 IPS |
|
|
Text Classification (F16)
|
100% |
391
561.7 IPS |
|
|
Text Classification (I8)
|
92% |
283
406.3 IPS |
|
|
Machine Translation (F32)
|
100% |
724
13.3 IPS |
|
|
Machine Translation (F16)
|
100% |
670
12.3 IPS |
|
|
Machine Translation (I8)
|
64% |
345
6.34 IPS |
