HONOR LGE-NX9
Geekbench ML Score
464
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 15 2024 01:40 PM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | HONOR LGE-NX9 |
| Model ID | HONOR LGE-NX9 |
| 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 | 1.78 GHz |
| Cluster 1 | 4 Cores @ 1.79 GHz |
| Cluster 2 | 3 Cores @ 2.50 GHz |
| Cluster 3 | 1 Core @ 3.00 GHz |
| Memory Information | |
|---|---|
| Size | 7.00 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
211
39.5 IPS |
|
|
Image Classification (F16)
|
100% |
228
42.6 IPS |
|
|
Image Classification (I8)
|
97% |
345
64.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
307
5.13 IPS |
|
|
Image Segmentation (F16)
|
100% |
303
5.06 IPS |
|
|
Image Segmentation (I8)
|
98% |
360
6.02 IPS |
|
|
Pose Estimation (F32)
|
100% |
583
0.71 IPS |
|
|
Pose Estimation (F16)
|
100% |
560
0.68 IPS |
|
|
Pose Estimation (I8)
|
100% |
2059
2.49 IPS |
|
|
Object Detection (F32)
|
100% |
245
18.3 IPS |
|
|
Object Detection (F16)
|
100% |
240
17.9 IPS |
|
|
Object Detection (I8)
|
61% |
372
27.8 IPS |
|
|
Face Detection (F32)
|
100% |
545
6.49 IPS |
|
|
Face Detection (F16)
|
100% |
537
6.38 IPS |
|
|
Face Detection (I8)
|
86% |
867
10.3 IPS |
|
|
Depth Estimation (F32)
|
100% |
566
4.39 IPS |
|
|
Depth Estimation (F16)
|
100% |
561
4.35 IPS |
|
|
Depth Estimation (I8)
|
95% |
990
7.68 IPS |
|
|
Style Transfer (F32)
|
100% |
1050
1.38 IPS |
|
|
Style Transfer (F16)
|
100% |
1108
1.46 IPS |
|
|
Style Transfer (I8)
|
98% |
2178
2.87 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
330
11.8 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
335
12.0 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
843
30.1 IPS |
|
|
Text Classification (F32)
|
100% |
265
381.2 IPS |
|
|
Text Classification (F16)
|
100% |
271
388.9 IPS |
|
|
Text Classification (I8)
|
92% |
188
269.7 IPS |
|
|
Machine Translation (F32)
|
100% |
491
9.03 IPS |
|
|
Machine Translation (F16)
|
100% |
487
8.96 IPS |
|
|
Machine Translation (I8)
|
64% |
208
3.83 IPS |
