OPPO CPH2695
Geekbench ML Score
267
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 30 2025 01:39 AM |
| Views | 3 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | OPPO CPH2695 |
| Model ID | OPPO CPH2695 |
| Motherboard | k6835v2_64 |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 4 part 3339 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.40 GHz |
| Memory Information | |
|---|---|
| Size | 7.43 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
132
24.6 IPS |
|
|
Image Classification (F16)
|
100% |
416
77.9 IPS |
|
|
Image Classification (I8)
|
97% |
21
3.92 IPS |
|
|
Image Segmentation (F32)
|
100% |
253
4.22 IPS |
|
|
Image Segmentation (F16)
|
100% |
480
8.01 IPS |
|
|
Image Segmentation (I8)
|
98% |
23
0.39 IPS |
|
|
Pose Estimation (F32)
|
100% |
895
1.08 IPS |
|
|
Pose Estimation (F16)
|
100% |
2084
2.52 IPS |
|
|
Pose Estimation (I8)
|
87% |
1180
1.43 IPS |
|
|
Object Detection (F32)
|
100% |
158
11.8 IPS |
|
|
Object Detection (F16)
|
100% |
331
24.7 IPS |
|
|
Object Detection (I8)
|
51% |
28
2.12 IPS |
|
|
Face Detection (F32)
|
25% |
702
8.34 IPS |
|
|
Face Detection (F16)
|
25% |
983
11.7 IPS |
|
|
Face Detection (I8)
|
87% |
37
0.44 IPS |
|
|
Depth Estimation (F32)
|
100% |
375
2.91 IPS |
|
|
Depth Estimation (F16)
|
74% |
478
3.71 IPS |
|
|
Depth Estimation (I8)
|
93% |
171
1.33 IPS |
|
|
Style Transfer (F32)
|
100% |
716
0.94 IPS |
|
|
Style Transfer (F16)
|
100% |
1285
1.69 IPS |
|
|
Style Transfer (I8)
|
98% |
1647
2.17 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
381
13.6 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
891
31.8 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
441
15.7 IPS |
|
|
Text Classification (F32)
|
100% |
78
112.6 IPS |
|
|
Text Classification (F16)
|
100% |
86
123.3 IPS |
|
|
Text Classification (I8)
|
94% |
63
90.8 IPS |
|
|
Machine Translation (F32)
|
100% |
213
3.92 IPS |
|
|
Machine Translation (F16)
|
100% |
290
5.33 IPS |
|
|
Machine Translation (I8)
|
57% |
135
2.49 IPS |
