OPPO CPH2737
Geekbench AI Score
1212
Single Precision Score
1618
Half Precision Score
1370
Quantized Score
Geekbench AI 1.5.0 for Android AArch64
Result Information
| Upload Date | October 21 2025 08:15 AM |
| Views | 1 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | ARM ARMv8 |
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | OPPO CPH2737 |
| Model ID | OPPO CPH2737 |
| Motherboard | k6897v1_64 |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3405 revision 0 |
| Base Frequency | 2.20 GHz |
| Cluster 1 | 4 Cores @ 2.20 GHz |
| Cluster 2 | 3 Cores @ 3.20 GHz |
| Cluster 3 | 1 Core @ 3.35 GHz |
| Memory Information | |
|---|---|
| Size | 11.21 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
505
93.9 IPS |
|
|
Image Classification (HP)
|
100% |
697
129.7 IPS |
|
|
Image Classification (Q)
|
97% |
665
124.2 IPS |
|
|
Image Segmentation (SP)
|
100% |
1462
23.7 IPS |
|
|
Image Segmentation (HP)
|
100% |
2284
37.0 IPS |
|
|
Image Segmentation (Q)
|
98% |
2075
33.7 IPS |
|
|
Pose Estimation (SP)
|
100% |
7366
8.60 IPS |
|
|
Pose Estimation (HP)
|
100% |
12691
14.8 IPS |
|
|
Pose Estimation (Q)
|
95% |
13116
15.4 IPS |
|
|
Object Detection (SP)
|
100% |
566
44.9 IPS |
|
|
Object Detection (HP)
|
99% |
804
63.8 IPS |
|
|
Object Detection (Q)
|
83% |
777
62.7 IPS |
|
|
Face Detection (SP)
|
100% |
2548
30.3 IPS |
|
|
Face Detection (HP)
|
100% |
3701
44.0 IPS |
|
|
Face Detection (Q)
|
96% |
3006
35.9 IPS |
|
|
Depth Estimation (SP)
|
100% |
3718
28.6 IPS |
|
|
Depth Estimation (HP)
|
98% |
5575
43.1 IPS |
|
|
Depth Estimation (Q)
|
62% |
4547
43.2 IPS |
|
|
Style Transfer (SP)
|
100% |
12042
15.5 IPS |
|
|
Style Transfer (HP)
|
100% |
20351
26.2 IPS |
|
|
Style Transfer (Q)
|
98% |
20149
26.0 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
1642
60.6 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
2732
100.9 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
2381
88.2 IPS |
|
|
Text Classification (SP)
|
35% |
26
232.4 IPS |
|
|
Text Classification (HP)
|
29% |
14
249.0 IPS |
|
|
Text Classification (Q)
|
32% |
18
237.1 IPS |
|
|
Machine Translation (SP)
|
100% |
460
7.92 IPS |
|
|
Machine Translation (HP)
|
100% |
472
8.13 IPS |
|
|
Machine Translation (Q)
|
43% |
141
8.19 IPS |
