motorola motorola razr 2023
Geekbench ML Score
249
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 08 2024 01:37 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | motorola motorola razr 2023 |
| Model ID | motorola motorola razr 2023 |
| Motherboard | lynkco |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 2 part 3399 revision 0 |
| Base Frequency | 1.80 GHz |
| Cluster 1 | 4 Cores @ 1.80 GHz |
| Cluster 2 | 3 Cores @ 2.36 GHz |
| Cluster 3 | 1 Core @ 2.52 GHz |
| Memory Information | |
|---|---|
| Size | 7.19 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
124
23.1 IPS |
|
|
Image Classification (F16)
|
100% |
123
23.0 IPS |
|
|
Image Classification (I8)
|
97% |
335
62.7 IPS |
|
|
Image Segmentation (F32)
|
100% |
164
2.74 IPS |
|
|
Image Segmentation (F16)
|
100% |
166
2.78 IPS |
|
|
Image Segmentation (I8)
|
98% |
361
6.02 IPS |
|
|
Pose Estimation (F32)
|
100% |
205
0.25 IPS |
|
|
Pose Estimation (F16)
|
100% |
201
0.24 IPS |
|
|
Pose Estimation (I8)
|
100% |
791
0.96 IPS |
|
|
Object Detection (F32)
|
100% |
115
8.57 IPS |
|
|
Object Detection (F16)
|
100% |
115
8.61 IPS |
|
|
Object Detection (I8)
|
61% |
334
24.9 IPS |
|
|
Face Detection (F32)
|
100% |
296
3.51 IPS |
|
|
Face Detection (F16)
|
100% |
309
3.67 IPS |
|
|
Face Detection (I8)
|
86% |
615
7.31 IPS |
|
|
Depth Estimation (F32)
|
100% |
226
1.75 IPS |
|
|
Depth Estimation (F16)
|
100% |
236
1.83 IPS |
|
|
Depth Estimation (I8)
|
95% |
675
5.24 IPS |
|
|
Style Transfer (F32)
|
100% |
402
0.53 IPS |
|
|
Style Transfer (F16)
|
100% |
373
0.49 IPS |
|
|
Style Transfer (I8)
|
98% |
1100
1.45 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
109
3.90 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
110
3.92 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
423
15.1 IPS |
|
|
Text Classification (F32)
|
100% |
125
179.3 IPS |
|
|
Text Classification (F16)
|
100% |
124
178.2 IPS |
|
|
Text Classification (I8)
|
92% |
250
358.9 IPS |
|
|
Machine Translation (F32)
|
100% |
229
4.21 IPS |
|
|
Machine Translation (F16)
|
100% |
235
4.32 IPS |
|
|
Machine Translation (I8)
|
64% |
307
5.64 IPS |
