OPPO CPH2305
Geekbench ML Score
317
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 15 2024 02:51 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 | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
138
25.9 IPS |
|
|
Image Classification (F16)
|
100% |
135
25.2 IPS |
|
|
Image Classification (I8)
|
97% |
370
69.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
179
2.99 IPS |
|
|
Image Segmentation (F16)
|
100% |
170
2.84 IPS |
|
|
Image Segmentation (I8)
|
98% |
377
6.29 IPS |
|
|
Pose Estimation (F32)
|
100% |
247
0.30 IPS |
|
|
Pose Estimation (F16)
|
100% |
240
0.29 IPS |
|
|
Pose Estimation (I8)
|
100% |
947
1.15 IPS |
|
|
Object Detection (F32)
|
100% |
142
10.6 IPS |
|
|
Object Detection (F16)
|
100% |
142
10.6 IPS |
|
|
Object Detection (I8)
|
61% |
419
31.3 IPS |
|
|
Face Detection (F32)
|
100% |
359
4.27 IPS |
|
|
Face Detection (F16)
|
100% |
372
4.42 IPS |
|
|
Face Detection (I8)
|
86% |
896
10.6 IPS |
|
|
Depth Estimation (F32)
|
100% |
309
2.40 IPS |
|
|
Depth Estimation (F16)
|
100% |
310
2.41 IPS |
|
|
Depth Estimation (I8)
|
95% |
889
6.89 IPS |
|
|
Style Transfer (F32)
|
100% |
548
0.72 IPS |
|
|
Style Transfer (F16)
|
100% |
541
0.71 IPS |
|
|
Style Transfer (I8)
|
98% |
1373
1.81 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
157
5.61 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
155
5.55 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
570
20.4 IPS |
|
|
Text Classification (F32)
|
100% |
186
267.1 IPS |
|
|
Text Classification (F16)
|
100% |
189
271.3 IPS |
|
|
Text Classification (I8)
|
92% |
334
479.9 IPS |
|
|
Machine Translation (F32)
|
100% |
338
6.22 IPS |
|
|
Machine Translation (F16)
|
100% |
332
6.10 IPS |
|
|
Machine Translation (I8)
|
64% |
399
7.34 IPS |
