OPPO CPH2643
Geekbench ML Score
406
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 17 2024 06:05 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | OPPO CPH2643 |
| Model ID | OPPO CPH2643 |
| Motherboard | k6877v1_64_k419 |
| CPU Information | |
|---|---|
| Name | ARM MT6877V/TTZA |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3393 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.60 GHz |
| Memory Information | |
|---|---|
| Size | 7.35 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
150
28.1 IPS |
|
|
Image Classification (F16)
|
100% |
178
33.3 IPS |
|
|
Image Classification (I8)
|
97% |
387
72.4 IPS |
|
|
Image Segmentation (F32)
|
100% |
211
3.52 IPS |
|
|
Image Segmentation (F16)
|
100% |
263
4.39 IPS |
|
|
Image Segmentation (I8)
|
98% |
460
7.68 IPS |
|
|
Pose Estimation (F32)
|
100% |
417
0.50 IPS |
|
|
Pose Estimation (F16)
|
100% |
415
0.50 IPS |
|
|
Pose Estimation (I8)
|
100% |
1712
2.07 IPS |
|
|
Object Detection (F32)
|
100% |
210
15.7 IPS |
|
|
Object Detection (F16)
|
100% |
202
15.1 IPS |
|
|
Object Detection (I8)
|
61% |
466
34.8 IPS |
|
|
Face Detection (F32)
|
100% |
446
5.30 IPS |
|
|
Face Detection (F16)
|
100% |
443
5.26 IPS |
|
|
Face Detection (I8)
|
86% |
950
11.3 IPS |
|
|
Depth Estimation (F32)
|
100% |
444
3.44 IPS |
|
|
Depth Estimation (F16)
|
100% |
461
3.57 IPS |
|
|
Depth Estimation (I8)
|
95% |
970
7.52 IPS |
|
|
Style Transfer (F32)
|
100% |
755
0.99 IPS |
|
|
Style Transfer (F16)
|
100% |
750
0.99 IPS |
|
|
Style Transfer (I8)
|
98% |
1971
2.59 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
248
8.85 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
249
8.90 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
682
24.3 IPS |
|
|
Text Classification (F32)
|
100% |
256
367.5 IPS |
|
|
Text Classification (F16)
|
100% |
202
289.9 IPS |
|
|
Text Classification (I8)
|
92% |
215
309.4 IPS |
|
|
Machine Translation (F32)
|
100% |
448
8.24 IPS |
|
|
Machine Translation (F16)
|
100% |
471
8.66 IPS |
|
|
Machine Translation (I8)
|
64% |
297
5.46 IPS |
