HONOR REA-NX9
Geekbench ML Score
426
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | March 27 2024 06:52 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | HONOR REA-NX9 |
| Model ID | HONOR REA-NX9 |
| Motherboard | taro |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 2 part 3399 revision 0 |
| Base Frequency | 1.80 GHz |
| Cluster 1 | 4 Cores @ 1.80 GHz |
| Cluster 2 | 3 Cores @ 2.36 GHz |
| Cluster 3 | 1 Core @ 2.52 GHz |
| Memory Information | |
|---|---|
| Size | 7.14 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
205
38.4 IPS |
|
|
Image Classification (F16)
|
100% |
195
36.5 IPS |
|
|
Image Classification (I8)
|
97% |
373
69.7 IPS |
|
|
Image Segmentation (F32)
|
100% |
291
4.85 IPS |
|
|
Image Segmentation (F16)
|
100% |
286
4.78 IPS |
|
|
Image Segmentation (I8)
|
98% |
348
5.82 IPS |
|
|
Pose Estimation (F32)
|
100% |
503
0.61 IPS |
|
|
Pose Estimation (F16)
|
100% |
495
0.60 IPS |
|
|
Pose Estimation (I8)
|
100% |
2016
2.44 IPS |
|
|
Object Detection (F32)
|
100% |
221
16.5 IPS |
|
|
Object Detection (F16)
|
100% |
225
16.8 IPS |
|
|
Object Detection (I8)
|
61% |
398
29.7 IPS |
|
|
Face Detection (F32)
|
100% |
476
5.66 IPS |
|
|
Face Detection (F16)
|
100% |
465
5.53 IPS |
|
|
Face Detection (I8)
|
86% |
826
9.82 IPS |
|
|
Depth Estimation (F32)
|
100% |
458
3.55 IPS |
|
|
Depth Estimation (F16)
|
100% |
495
3.84 IPS |
|
|
Depth Estimation (I8)
|
95% |
976
7.57 IPS |
|
|
Style Transfer (F32)
|
100% |
869
1.14 IPS |
|
|
Style Transfer (F16)
|
100% |
845
1.11 IPS |
|
|
Style Transfer (I8)
|
98% |
1980
2.60 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
268
9.57 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
258
9.21 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
804
28.7 IPS |
|
|
Text Classification (F32)
|
100% |
238
341.5 IPS |
|
|
Text Classification (F16)
|
100% |
249
358.1 IPS |
|
|
Text Classification (I8)
|
92% |
186
267.6 IPS |
|
|
Machine Translation (F32)
|
100% |
477
8.77 IPS |
|
|
Machine Translation (F16)
|
100% |
468
8.61 IPS |
|
|
Machine Translation (I8)
|
64% |
221
4.06 IPS |
