HONOR CRT-NX1
Geekbench ML Score
398
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 12 2024 05:09 PM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | HONOR CRT-NX1 |
| Model ID | HONOR CRT-NX1 |
| Motherboard | CRTN |
| CPU Information | |
|---|---|
| Name | ARM MT6833 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 4 part 3339 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.20 GHz |
| Memory Information | |
|---|---|
| Size | 7.50 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
195
36.5 IPS |
|
|
Image Classification (F16)
|
100% |
190
35.6 IPS |
|
|
Image Classification (I8)
|
97% |
311
58.3 IPS |
|
|
Image Segmentation (F32)
|
100% |
299
5.00 IPS |
|
|
Image Segmentation (F16)
|
100% |
298
4.98 IPS |
|
|
Image Segmentation (I8)
|
98% |
405
6.76 IPS |
|
|
Pose Estimation (F32)
|
100% |
405
0.49 IPS |
|
|
Pose Estimation (F16)
|
100% |
400
0.48 IPS |
|
|
Pose Estimation (I8)
|
100% |
1852
2.24 IPS |
|
|
Object Detection (F32)
|
100% |
216
16.2 IPS |
|
|
Object Detection (F16)
|
100% |
226
16.8 IPS |
|
|
Object Detection (I8)
|
61% |
400
29.8 IPS |
|
|
Face Detection (F32)
|
100% |
443
5.27 IPS |
|
|
Face Detection (F16)
|
100% |
448
5.33 IPS |
|
|
Face Detection (I8)
|
86% |
908
10.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
431
3.35 IPS |
|
|
Depth Estimation (F16)
|
100% |
428
3.32 IPS |
|
|
Depth Estimation (I8)
|
95% |
893
6.93 IPS |
|
|
Style Transfer (F32)
|
100% |
664
0.87 IPS |
|
|
Style Transfer (F16)
|
100% |
664
0.87 IPS |
|
|
Style Transfer (I8)
|
98% |
2047
2.69 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
244
8.70 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
242
8.63 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
702
25.1 IPS |
|
|
Text Classification (F32)
|
100% |
210
301.7 IPS |
|
|
Text Classification (F16)
|
100% |
211
303.7 IPS |
|
|
Text Classification (I8)
|
92% |
197
283.4 IPS |
|
|
Machine Translation (F32)
|
100% |
473
8.70 IPS |
|
|
Machine Translation (F16)
|
100% |
477
8.78 IPS |
|
|
Machine Translation (I8)
|
64% |
196
3.60 IPS |
