OPPO CPH2499
Geekbench ML Score
991
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | January 16 2024 01:55 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | OPPO CPH2499 |
| Model ID | OPPO CPH2499 |
| Motherboard | kalama |
| Governor | uag |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3406 revision 0 |
| Base Frequency | 2.02 GHz |
| Cluster 1 | 3 Cores @ 2.02 GHz |
| Cluster 2 | 4 Cores @ 2.80 GHz |
| Cluster 3 | 1 Core @ 3.19 GHz |
| Memory Information | |
|---|---|
| Size | 14.83 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
480
89.8 IPS |
|
|
Image Classification (F16)
|
100% |
478
89.4 IPS |
|
|
Image Classification (I8)
|
97% |
757
141.7 IPS |
|
|
Image Segmentation (F32)
|
100% |
593
9.90 IPS |
|
|
Image Segmentation (F16)
|
100% |
626
10.5 IPS |
|
|
Image Segmentation (I8)
|
98% |
570
9.51 IPS |
|
|
Pose Estimation (F32)
|
100% |
1247
1.51 IPS |
|
|
Pose Estimation (F16)
|
100% |
1228
1.49 IPS |
|
|
Pose Estimation (I8)
|
100% |
4946
5.99 IPS |
|
|
Object Detection (F32)
|
100% |
500
37.3 IPS |
|
|
Object Detection (F16)
|
100% |
499
37.2 IPS |
|
|
Object Detection (I8)
|
61% |
1052
78.5 IPS |
|
|
Face Detection (F32)
|
100% |
1060
12.6 IPS |
|
|
Face Detection (F16)
|
100% |
1080
12.8 IPS |
|
|
Face Detection (I8)
|
86% |
1909
22.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
1220
9.46 IPS |
|
|
Depth Estimation (F16)
|
100% |
1210
9.38 IPS |
|
|
Depth Estimation (I8)
|
95% |
2063
16.0 IPS |
|
|
Style Transfer (F32)
|
100% |
2282
3.00 IPS |
|
|
Style Transfer (F16)
|
100% |
2257
2.97 IPS |
|
|
Style Transfer (I8)
|
98% |
4988
6.56 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
713
25.5 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
745
26.6 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1803
64.4 IPS |
|
|
Text Classification (F32)
|
100% |
540
776.4 IPS |
|
|
Text Classification (F16)
|
100% |
562
807.9 IPS |
|
|
Text Classification (I8)
|
92% |
415
596.5 IPS |
|
|
Machine Translation (F32)
|
100% |
1142
21.0 IPS |
|
|
Machine Translation (F16)
|
100% |
1077
19.8 IPS |
|
|
Machine Translation (I8)
|
64% |
424
7.79 IPS |
