HONOR JLH-AN00
Geekbench ML Score
413
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 11 2025 10:51 AM |
| Views | 7 |
System Information
| System Information | |
|---|---|
| Operating System | Android 12 |
| Model | HONOR JLH-AN00 |
| Model ID | HONOR JLH-AN00 |
| Motherboard | JLH |
| CPU Information | |
|---|---|
| Name | ARM MT6877V/ZA |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3393 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.40 GHz |
| Memory Information | |
|---|---|
| Size | 7.47 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
170
31.9 IPS |
|
|
Image Classification (F16)
|
100% |
176
32.9 IPS |
|
|
Image Classification (I8)
|
97% |
332
62.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
270
4.50 IPS |
|
|
Image Segmentation (F16)
|
100% |
287
4.80 IPS |
|
|
Image Segmentation (I8)
|
98% |
443
7.39 IPS |
|
|
Pose Estimation (F32)
|
100% |
437
0.53 IPS |
|
|
Pose Estimation (F16)
|
100% |
429
0.52 IPS |
|
|
Pose Estimation (I8)
|
100% |
1901
2.30 IPS |
|
|
Object Detection (F32)
|
100% |
211
15.8 IPS |
|
|
Object Detection (F16)
|
100% |
215
16.1 IPS |
|
|
Object Detection (I8)
|
61% |
385
28.7 IPS |
|
|
Face Detection (F32)
|
100% |
437
5.20 IPS |
|
|
Face Detection (F16)
|
100% |
437
5.19 IPS |
|
|
Face Detection (I8)
|
86% |
880
10.5 IPS |
|
|
Depth Estimation (F32)
|
100% |
421
3.26 IPS |
|
|
Depth Estimation (F16)
|
100% |
439
3.40 IPS |
|
|
Depth Estimation (I8)
|
95% |
837
6.49 IPS |
|
|
Style Transfer (F32)
|
100% |
756
0.99 IPS |
|
|
Style Transfer (F16)
|
100% |
748
0.98 IPS |
|
|
Style Transfer (I8)
|
98% |
2006
2.64 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
256
9.14 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
258
9.22 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
707
25.3 IPS |
|
|
Text Classification (F32)
|
100% |
276
396.6 IPS |
|
|
Text Classification (F16)
|
100% |
285
409.6 IPS |
|
|
Text Classification (I8)
|
92% |
263
377.7 IPS |
|
|
Machine Translation (F32)
|
100% |
402
7.40 IPS |
|
|
Machine Translation (F16)
|
100% |
400
7.37 IPS |
|
|
Machine Translation (I8)
|
64% |
329
6.06 IPS |
