HONOR VER-N49
Geekbench ML Score
864
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | January 17 2024 09:07 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | HONOR VER-N49 |
| Model ID | HONOR VER-N49 |
| Motherboard | kalama |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3406 revision 0 |
| Base Frequency | 2.02 GHz |
| Cluster 1 | 3 Cores @ 2.02 GHz |
| Cluster 2 | 4 Cores @ 2.80 GHz |
| Cluster 3 | 1 Core @ 3.19 GHz |
| Memory Information | |
|---|---|
| Size | 14.95 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
511
95.6 IPS |
|
|
Image Classification (F16)
|
100% |
490
91.7 IPS |
|
|
Image Classification (I8)
|
97% |
839
157.1 IPS |
|
|
Image Segmentation (F32)
|
100% |
494
8.25 IPS |
|
|
Image Segmentation (F16)
|
100% |
508
8.49 IPS |
|
|
Image Segmentation (I8)
|
98% |
603
10.1 IPS |
|
|
Pose Estimation (F32)
|
100% |
1210
1.47 IPS |
|
|
Pose Estimation (F16)
|
100% |
1147
1.39 IPS |
|
|
Pose Estimation (I8)
|
100% |
4215
5.10 IPS |
|
|
Object Detection (F32)
|
100% |
424
31.6 IPS |
|
|
Object Detection (F16)
|
100% |
503
37.6 IPS |
|
|
Object Detection (I8)
|
61% |
806
60.2 IPS |
|
|
Face Detection (F32)
|
100% |
761
9.05 IPS |
|
|
Face Detection (F16)
|
100% |
761
9.05 IPS |
|
|
Face Detection (I8)
|
86% |
1386
16.5 IPS |
|
|
Depth Estimation (F32)
|
100% |
933
7.23 IPS |
|
|
Depth Estimation (F16)
|
100% |
944
7.32 IPS |
|
|
Depth Estimation (I8)
|
95% |
1661
12.9 IPS |
|
|
Style Transfer (F32)
|
100% |
1938
2.55 IPS |
|
|
Style Transfer (F16)
|
100% |
1950
2.57 IPS |
|
|
Style Transfer (I8)
|
98% |
3694
4.86 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
672
24.0 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
701
25.0 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1205
43.0 IPS |
|
|
Text Classification (F32)
|
100% |
502
720.8 IPS |
|
|
Text Classification (F16)
|
100% |
501
719.7 IPS |
|
|
Text Classification (I8)
|
92% |
334
480.6 IPS |
|
|
Machine Translation (F32)
|
100% |
1053
19.4 IPS |
|
|
Machine Translation (F16)
|
100% |
1023
18.8 IPS |
|
|
Machine Translation (I8)
|
64% |
479
8.82 IPS |
