OnePlus 10T
Geekbench ML Score
809
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | March 28 2024 04:55 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 | 7.06 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
565
105.8 IPS |
|
|
Image Classification (F16)
|
100% |
652
122.1 IPS |
|
|
Image Classification (I8)
|
97% |
640
119.7 IPS |
|
|
Image Segmentation (F32)
|
100% |
737
12.3 IPS |
|
|
Image Segmentation (F16)
|
100% |
951
15.9 IPS |
|
|
Image Segmentation (I8)
|
98% |
851
14.2 IPS |
|
|
Pose Estimation (F32)
|
100% |
731
0.88 IPS |
|
|
Pose Estimation (F16)
|
100% |
1240
1.50 IPS |
|
|
Pose Estimation (I8)
|
100% |
1338
1.62 IPS |
|
|
Object Detection (F32)
|
100% |
333
24.9 IPS |
|
|
Object Detection (F16)
|
100% |
348
26.0 IPS |
|
|
Object Detection (I8)
|
62% |
419
31.3 IPS |
|
|
Face Detection (F32)
|
100% |
1772
21.1 IPS |
|
|
Face Detection (F16)
|
97% |
2128
25.3 IPS |
|
|
Face Detection (I8)
|
87% |
2203
26.2 IPS |
|
|
Depth Estimation (F32)
|
100% |
1132
8.78 IPS |
|
|
Depth Estimation (F16)
|
100% |
2627
20.4 IPS |
|
|
Depth Estimation (I8)
|
95% |
2625
20.4 IPS |
|
|
Style Transfer (F32)
|
100% |
2738
3.60 IPS |
|
|
Style Transfer (F16)
|
100% |
5366
7.06 IPS |
|
|
Style Transfer (I8)
|
98% |
5529
7.27 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
335
11.9 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
446
15.9 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
440
15.7 IPS |
|
|
Text Classification (F32)
|
100% |
177
254.8 IPS |
|
|
Text Classification (F16)
|
100% |
177
254.1 IPS |
|
|
Text Classification (I8)
|
92% |
357
512.7 IPS |
|
|
Machine Translation (F32)
|
100% |
329
6.06 IPS |
|
|
Machine Translation (F16)
|
100% |
358
6.58 IPS |
|
|
Machine Translation (I8)
|
64% |
460
8.47 IPS |
