OPPO CPH2739
Geekbench ML Score
1120
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | June 26 2025 08:32 AM |
| Views | 3 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | OPPO CPH2739 |
| Model ID | OPPO CPH2739 |
| Motherboard | k6899v1_64 |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3463 revision 1 |
| Base Frequency | 2.10 GHz |
| Cluster 1 | 4 Cores @ 2.10 GHz |
| Cluster 2 | 3 Cores @ 3.00 GHz |
| Cluster 3 | 1 Core @ 3.25 GHz |
| Memory Information | |
|---|---|
| Size | 11.19 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
690
129.2 IPS |
|
|
Image Classification (F16)
|
100% |
724
135.6 IPS |
|
|
Image Classification (I8)
|
97% |
822
153.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
791
13.2 IPS |
|
|
Image Segmentation (F16)
|
100% |
806
13.5 IPS |
|
|
Image Segmentation (I8)
|
98% |
782
13.1 IPS |
|
|
Pose Estimation (F32)
|
100% |
1119
1.35 IPS |
|
|
Pose Estimation (F16)
|
100% |
1132
1.37 IPS |
|
|
Pose Estimation (I8)
|
100% |
4627
5.60 IPS |
|
|
Object Detection (F32)
|
100% |
701
52.3 IPS |
|
|
Object Detection (F16)
|
100% |
726
54.2 IPS |
|
|
Object Detection (I8)
|
61% |
1061
79.2 IPS |
|
|
Face Detection (F32)
|
100% |
1677
19.9 IPS |
|
|
Face Detection (F16)
|
100% |
1674
19.9 IPS |
|
|
Face Detection (I8)
|
86% |
1805
21.5 IPS |
|
|
Depth Estimation (F32)
|
100% |
1299
10.1 IPS |
|
|
Depth Estimation (F16)
|
100% |
1339
10.4 IPS |
|
|
Depth Estimation (I8)
|
95% |
1931
15.0 IPS |
|
|
Style Transfer (F32)
|
100% |
1810
2.38 IPS |
|
|
Style Transfer (F16)
|
100% |
1789
2.35 IPS |
|
|
Style Transfer (I8)
|
98% |
4546
5.98 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
766
27.4 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
762
27.2 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1828
65.3 IPS |
|
|
Text Classification (F32)
|
100% |
786
1.13 KIPS |
|
|
Text Classification (F16)
|
100% |
783
1.13 KIPS |
|
|
Text Classification (I8)
|
92% |
409
588.2 IPS |
|
|
Machine Translation (F32)
|
100% |
1432
26.4 IPS |
|
|
Machine Translation (F16)
|
100% |
1406
25.9 IPS |
|
|
Machine Translation (I8)
|
64% |
346
6.37 IPS |
