HUAWEI TET-AN00
Geekbench ML Score
414
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 31 2025 12:11 PM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Android 12 |
| Model | HUAWEI TET-AN00 |
| Model ID | HUAWEI TET-AN00 |
| Motherboard | TET |
| Governor | schedutil |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3341 revision 0 |
| Base Frequency | 2.04 GHz |
| Cluster 1 | 4 Cores @ 2.04 GHz |
| Cluster 2 | 3 Cores @ 2.54 GHz |
| Cluster 3 | 1 Core @ 3.13 GHz |
| Memory Information | |
|---|---|
| Size | 7.32 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
196
36.7 IPS |
|
|
Image Classification (F16)
|
100% |
173
32.3 IPS |
|
|
Image Classification (I8)
|
97% |
353
66.0 IPS |
|
|
Image Segmentation (F32)
|
100% |
283
4.72 IPS |
|
|
Image Segmentation (F16)
|
100% |
280
4.68 IPS |
|
|
Image Segmentation (I8)
|
98% |
372
6.21 IPS |
|
|
Pose Estimation (F32)
|
100% |
445
0.54 IPS |
|
|
Pose Estimation (F16)
|
100% |
450
0.55 IPS |
|
|
Pose Estimation (I8)
|
100% |
2047
2.48 IPS |
|
|
Object Detection (F32)
|
100% |
204
15.3 IPS |
|
|
Object Detection (F16)
|
100% |
201
15.0 IPS |
|
|
Object Detection (I8)
|
61% |
486
36.3 IPS |
|
|
Face Detection (F32)
|
100% |
474
5.64 IPS |
|
|
Face Detection (F16)
|
100% |
450
5.35 IPS |
|
|
Face Detection (I8)
|
86% |
1008
12.0 IPS |
|
|
Depth Estimation (F32)
|
100% |
440
3.41 IPS |
|
|
Depth Estimation (F16)
|
100% |
449
3.48 IPS |
|
|
Depth Estimation (I8)
|
95% |
954
7.40 IPS |
|
|
Style Transfer (F32)
|
100% |
796
1.05 IPS |
|
|
Style Transfer (F16)
|
100% |
811
1.07 IPS |
|
|
Style Transfer (I8)
|
98% |
2197
2.89 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
256
9.13 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
266
9.52 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
897
32.0 IPS |
|
|
Text Classification (F32)
|
100% |
235
337.5 IPS |
|
|
Text Classification (F16)
|
100% |
230
330.3 IPS |
|
|
Text Classification (I8)
|
92% |
166
238.9 IPS |
|
|
Machine Translation (F32)
|
100% |
394
7.24 IPS |
|
|
Machine Translation (F16)
|
100% |
397
7.31 IPS |
|
|
Machine Translation (I8)
|
64% |
244
4.50 IPS |
