OPPO PJT110
Geekbench ML Score
238
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 02 2025 06:01 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | OPPO PJT110 |
| Model ID | OPPO PJT110 |
| Motherboard | blair |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3393 revision 1 |
| Base Frequency | 1.80 GHz |
| Cluster 1 | 6 Cores @ 1.80 GHz |
| Cluster 2 | 2 Cores @ 2.21 GHz |
| Memory Information | |
|---|---|
| Size | 11.07 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
108
20.2 IPS |
|
|
Image Classification (F16)
|
100% |
183
34.2 IPS |
|
|
Image Classification (I8)
|
99% |
187
34.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
143
2.39 IPS |
|
|
Image Segmentation (F16)
|
100% |
311
5.19 IPS |
|
|
Image Segmentation (I8)
|
98% |
310
5.18 IPS |
|
|
Pose Estimation (F32)
|
100% |
210
0.25 IPS |
|
|
Pose Estimation (F16)
|
100% |
211
0.26 IPS |
|
|
Pose Estimation (I8)
|
100% |
212
0.26 IPS |
|
|
Object Detection (F32)
|
100% |
101
7.54 IPS |
|
|
Object Detection (F16)
|
100% |
135
10.0 IPS |
|
|
Object Detection (I8)
|
62% |
165
12.3 IPS |
|
|
Face Detection (F32)
|
100% |
267
3.17 IPS |
|
|
Face Detection (F16)
|
97% |
550
6.54 IPS |
|
|
Face Detection (I8)
|
86% |
608
7.22 IPS |
|
|
Depth Estimation (F32)
|
100% |
237
1.84 IPS |
|
|
Depth Estimation (F16)
|
100% |
419
3.25 IPS |
|
|
Depth Estimation (I8)
|
95% |
424
3.29 IPS |
|
|
Style Transfer (F32)
|
100% |
449
0.59 IPS |
|
|
Style Transfer (F16)
|
100% |
1012
1.33 IPS |
|
|
Style Transfer (I8)
|
98% |
1015
1.34 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
119
4.26 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
234
8.37 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
246
8.78 IPS |
|
|
Text Classification (F32)
|
100% |
89
128.3 IPS |
|
|
Text Classification (F16)
|
100% |
89
128.4 IPS |
|
|
Text Classification (I8)
|
92% |
216
309.9 IPS |
|
|
Machine Translation (F32)
|
100% |
180
3.32 IPS |
|
|
Machine Translation (F16)
|
100% |
178
3.27 IPS |
|
|
Machine Translation (I8)
|
64% |
298
5.49 IPS |
