OPPO PJT110
Geekbench ML Score
179
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 02 2025 06:12 PM |
| Views | 6 |
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 | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
73
13.6 IPS |
|
|
Image Classification (F16)
|
100% |
73
13.6 IPS |
|
|
Image Classification (I8)
|
97% |
229
42.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
98
1.63 IPS |
|
|
Image Segmentation (F16)
|
100% |
98
1.63 IPS |
|
|
Image Segmentation (I8)
|
98% |
264
4.42 IPS |
|
|
Pose Estimation (F32)
|
100% |
122
0.15 IPS |
|
|
Pose Estimation (F16)
|
100% |
122
0.15 IPS |
|
|
Pose Estimation (I8)
|
100% |
501
0.61 IPS |
|
|
Object Detection (F32)
|
100% |
76
5.64 IPS |
|
|
Object Detection (F16)
|
100% |
76
5.65 IPS |
|
|
Object Detection (I8)
|
61% |
247
18.5 IPS |
|
|
Face Detection (F32)
|
100% |
212
2.53 IPS |
|
|
Face Detection (F16)
|
100% |
213
2.53 IPS |
|
|
Face Detection (I8)
|
86% |
549
6.53 IPS |
|
|
Depth Estimation (F32)
|
100% |
165
1.28 IPS |
|
|
Depth Estimation (F16)
|
100% |
165
1.28 IPS |
|
|
Depth Estimation (I8)
|
95% |
545
4.22 IPS |
|
|
Style Transfer (F32)
|
100% |
295
0.39 IPS |
|
|
Style Transfer (F16)
|
100% |
295
0.39 IPS |
|
|
Style Transfer (I8)
|
98% |
951
1.25 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
81
2.91 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
81
2.90 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
307
11.0 IPS |
|
|
Text Classification (F32)
|
100% |
95
136.8 IPS |
|
|
Text Classification (F16)
|
100% |
95
136.9 IPS |
|
|
Text Classification (I8)
|
92% |
233
334.5 IPS |
|
|
Machine Translation (F32)
|
100% |
181
3.34 IPS |
|
|
Machine Translation (F16)
|
100% |
181
3.33 IPS |
|
|
Machine Translation (I8)
|
64% |
299
5.50 IPS |
