OPPO PGEM10
Geekbench ML Score
896
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 23 2024 04:47 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | OPPO PGEM10 |
| Model ID | OPPO PGEM10 |
| 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% |
475
88.9 IPS |
|
|
Image Classification (F16)
|
100% |
488
91.3 IPS |
|
|
Image Classification (I8)
|
97% |
798
149.3 IPS |
|
|
Image Segmentation (F32)
|
100% |
616
10.3 IPS |
|
|
Image Segmentation (F16)
|
100% |
610
10.2 IPS |
|
|
Image Segmentation (I8)
|
98% |
571
9.54 IPS |
|
|
Pose Estimation (F32)
|
100% |
1244
1.51 IPS |
|
|
Pose Estimation (F16)
|
100% |
1159
1.40 IPS |
|
|
Pose Estimation (I8)
|
100% |
4179
5.06 IPS |
|
|
Object Detection (F32)
|
100% |
476
35.5 IPS |
|
|
Object Detection (F16)
|
100% |
453
33.8 IPS |
|
|
Object Detection (I8)
|
61% |
874
65.3 IPS |
|
|
Face Detection (F32)
|
100% |
1038
12.3 IPS |
|
|
Face Detection (F16)
|
100% |
1042
12.4 IPS |
|
|
Face Detection (I8)
|
86% |
1595
19.0 IPS |
|
|
Depth Estimation (F32)
|
100% |
1147
8.90 IPS |
|
|
Depth Estimation (F16)
|
100% |
1111
8.62 IPS |
|
|
Depth Estimation (I8)
|
95% |
1879
14.6 IPS |
|
|
Style Transfer (F32)
|
100% |
2030
2.67 IPS |
|
|
Style Transfer (F16)
|
100% |
1955
2.57 IPS |
|
|
Style Transfer (I8)
|
98% |
3354
4.41 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
635
22.7 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
660
23.6 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1539
54.9 IPS |
|
|
Text Classification (F32)
|
100% |
492
706.4 IPS |
|
|
Text Classification (F16)
|
100% |
479
687.9 IPS |
|
|
Text Classification (I8)
|
92% |
335
481.1 IPS |
|
|
Machine Translation (F32)
|
100% |
1010
18.6 IPS |
|
|
Machine Translation (F16)
|
100% |
1009
18.6 IPS |
|
|
Machine Translation (I8)
|
64% |
289
5.32 IPS |
