HONOR AGT-AN00
Geekbench ML Score
373
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | December 23 2024 11:17 AM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | HONOR AGT-AN00 |
| Model ID | HONOR AGT-AN00 |
| Motherboard | taro |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM SM8475 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 2 part 3400 revision 0 |
| Base Frequency | 1.80 GHz |
| Cluster 1 | 4 Cores @ 1.80 GHz |
| Cluster 2 | 3 Cores @ 2.50 GHz |
| Cluster 3 | 1 Core @ 3.00 GHz |
| Memory Information | |
|---|---|
| Size | 11.03 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
196
36.7 IPS |
|
|
Image Classification (F16)
|
100% |
156
29.3 IPS |
|
|
Image Classification (I8)
|
97% |
429
80.3 IPS |
|
|
Image Segmentation (F32)
|
100% |
201
3.36 IPS |
|
|
Image Segmentation (F16)
|
100% |
208
3.47 IPS |
|
|
Image Segmentation (I8)
|
98% |
536
8.95 IPS |
|
|
Pose Estimation (F32)
|
100% |
275
0.33 IPS |
|
|
Pose Estimation (F16)
|
100% |
279
0.34 IPS |
|
|
Pose Estimation (I8)
|
100% |
1058
1.28 IPS |
|
|
Object Detection (F32)
|
100% |
162
12.1 IPS |
|
|
Object Detection (F16)
|
100% |
159
11.9 IPS |
|
|
Object Detection (I8)
|
61% |
488
36.5 IPS |
|
|
Face Detection (F32)
|
100% |
427
5.08 IPS |
|
|
Face Detection (F16)
|
100% |
426
5.06 IPS |
|
|
Face Detection (I8)
|
86% |
1064
12.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
354
2.74 IPS |
|
|
Depth Estimation (F16)
|
100% |
352
2.73 IPS |
|
|
Depth Estimation (I8)
|
95% |
1032
8.00 IPS |
|
|
Style Transfer (F32)
|
100% |
601
0.79 IPS |
|
|
Style Transfer (F16)
|
100% |
648
0.85 IPS |
|
|
Style Transfer (I8)
|
98% |
1565
2.06 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
181
6.46 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
180
6.44 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
671
24.0 IPS |
|
|
Text Classification (F32)
|
100% |
211
303.1 IPS |
|
|
Text Classification (F16)
|
100% |
211
302.6 IPS |
|
|
Text Classification (I8)
|
92% |
411
591.1 IPS |
|
|
Machine Translation (F32)
|
100% |
386
7.10 IPS |
|
|
Machine Translation (F16)
|
100% |
448
8.25 IPS |
|
|
Machine Translation (I8)
|
64% |
458
8.44 IPS |
