OPPO CPH2737
Geekbench AI Score
1205
Single Precision Score
1222
Half Precision Score
1914
Quantized Score
Geekbench AI 1.5.0 for Android AArch64
Result Information
| Upload Date | December 10 2025 06:15 AM |
| Views | 4 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | ARM ARMv8 |
| System Information | |
|---|---|
| Operating System | Android 16 |
| 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 | 7.30 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
1336
248.4 IPS |
|
|
Image Classification (HP)
|
100% |
1396
259.6 IPS |
|
|
Image Classification (Q)
|
100% |
1505
279.8 IPS |
|
|
Image Segmentation (SP)
|
100% |
979
15.9 IPS |
|
|
Image Segmentation (HP)
|
100% |
975
15.8 IPS |
|
|
Image Segmentation (Q)
|
98% |
1606
26.1 IPS |
|
|
Pose Estimation (SP)
|
100% |
1910
2.23 IPS |
|
|
Pose Estimation (HP)
|
100% |
1954
2.28 IPS |
|
|
Pose Estimation (Q)
|
84% |
4216
5.00 IPS |
|
|
Object Detection (SP)
|
98% |
793
63.1 IPS |
|
|
Object Detection (HP)
|
98% |
676
53.8 IPS |
|
|
Object Detection (Q)
|
83% |
1151
93.0 IPS |
|
|
Face Detection (SP)
|
100% |
1565
18.6 IPS |
|
|
Face Detection (HP)
|
100% |
1689
20.1 IPS |
|
|
Face Detection (Q)
|
95% |
2818
33.6 IPS |
|
|
Depth Estimation (SP)
|
99% |
1696
13.1 IPS |
|
|
Depth Estimation (HP)
|
99% |
1780
13.8 IPS |
|
|
Depth Estimation (Q)
|
64% |
3117
28.5 IPS |
|
|
Style Transfer (SP)
|
89% |
4774
6.20 IPS |
|
|
Style Transfer (HP)
|
89% |
4727
6.14 IPS |
|
|
Style Transfer (Q)
|
98% |
9559
12.3 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
797
29.4 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
848
31.3 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
2016
74.7 IPS |
|
|
Text Classification (SP)
|
100% |
366
488.0 IPS |
|
|
Text Classification (HP)
|
99% |
386
515.7 IPS |
|
|
Text Classification (Q)
|
88% |
751
1.01 KIPS |
|
|
Machine Translation (SP)
|
100% |
882
15.2 IPS |
|
|
Machine Translation (HP)
|
100% |
892
15.4 IPS |
|
|
Machine Translation (Q)
|
50% |
443
15.7 IPS |
