OnePlus 10T
Geekbench ML Score
372
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 26 2024 01:14 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | OnePlus 10T |
| Model ID | OnePlus CPH2415 |
| Motherboard | taro |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 2 part 3400 revision 0 |
| Base Frequency | 2.02 GHz |
| Cluster 1 | 4 Cores @ 2.02 GHz |
| Cluster 2 | 3 Cores @ 2.75 GHz |
| Cluster 3 | 1 Core @ 3.19 GHz |
| Memory Information | |
|---|---|
| Size | 14.86 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
155
29.1 IPS |
|
|
Image Classification (F16)
|
100% |
156
29.2 IPS |
|
|
Image Classification (I8)
|
97% |
430
80.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
198
3.31 IPS |
|
|
Image Segmentation (F16)
|
100% |
199
3.33 IPS |
|
|
Image Segmentation (I8)
|
98% |
485
8.10 IPS |
|
|
Pose Estimation (F32)
|
100% |
270
0.33 IPS |
|
|
Pose Estimation (F16)
|
100% |
268
0.32 IPS |
|
|
Pose Estimation (I8)
|
100% |
1070
1.30 IPS |
|
|
Object Detection (F32)
|
100% |
163
12.1 IPS |
|
|
Object Detection (F16)
|
100% |
165
12.3 IPS |
|
|
Object Detection (I8)
|
61% |
531
39.7 IPS |
|
|
Face Detection (F32)
|
100% |
427
5.07 IPS |
|
|
Face Detection (F16)
|
100% |
432
5.13 IPS |
|
|
Face Detection (I8)
|
86% |
1051
12.5 IPS |
|
|
Depth Estimation (F32)
|
100% |
352
2.73 IPS |
|
|
Depth Estimation (F16)
|
100% |
353
2.74 IPS |
|
|
Depth Estimation (I8)
|
95% |
1061
8.23 IPS |
|
|
Style Transfer (F32)
|
100% |
602
0.79 IPS |
|
|
Style Transfer (F16)
|
100% |
603
0.79 IPS |
|
|
Style Transfer (I8)
|
98% |
1554
2.04 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
181
6.47 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
183
6.54 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
758
27.1 IPS |
|
|
Text Classification (F32)
|
100% |
221
318.0 IPS |
|
|
Text Classification (F16)
|
100% |
253
362.9 IPS |
|
|
Text Classification (I8)
|
92% |
456
655.2 IPS |
|
|
Machine Translation (F32)
|
100% |
382
7.03 IPS |
|
|
Machine Translation (F16)
|
100% |
384
7.06 IPS |
|
|
Machine Translation (I8)
|
64% |
457
8.40 IPS |
