realme RMX3710
Geekbench ML Score
278
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 09 2024 10:59 AM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | realme RMX3710 |
| Model ID | realme RMX3710 |
| Motherboard | RM6769 |
| CPU Information | |
|---|---|
| Name | ARM MT6769H |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 3 part 3338 revision 1 |
| Base Frequency | 1.80 GHz |
| Cluster 1 | 6 Cores @ 1.80 GHz |
| Cluster 2 | 2 Cores @ 2.00 GHz |
| Memory Information | |
|---|---|
| Size | 5.59 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
132
24.7 IPS |
|
|
Image Classification (F16)
|
100% |
128
24.0 IPS |
|
|
Image Classification (I8)
|
97% |
280
52.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
208
3.47 IPS |
|
|
Image Segmentation (F16)
|
100% |
212
3.54 IPS |
|
|
Image Segmentation (I8)
|
98% |
262
4.37 IPS |
|
|
Pose Estimation (F32)
|
100% |
257
0.31 IPS |
|
|
Pose Estimation (F16)
|
100% |
260
0.32 IPS |
|
|
Pose Estimation (I8)
|
100% |
1185
1.43 IPS |
|
|
Object Detection (F32)
|
100% |
159
11.9 IPS |
|
|
Object Detection (F16)
|
100% |
161
12.0 IPS |
|
|
Object Detection (I8)
|
61% |
338
25.2 IPS |
|
|
Face Detection (F32)
|
100% |
311
3.70 IPS |
|
|
Face Detection (F16)
|
100% |
333
3.95 IPS |
|
|
Face Detection (I8)
|
86% |
652
7.75 IPS |
|
|
Depth Estimation (F32)
|
100% |
316
2.45 IPS |
|
|
Depth Estimation (F16)
|
100% |
298
2.31 IPS |
|
|
Depth Estimation (I8)
|
95% |
633
4.91 IPS |
|
|
Style Transfer (F32)
|
100% |
483
0.64 IPS |
|
|
Style Transfer (F16)
|
100% |
503
0.66 IPS |
|
|
Style Transfer (I8)
|
98% |
1363
1.79 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
129
4.59 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
158
5.65 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
531
18.9 IPS |
|
|
Text Classification (F32)
|
100% |
166
238.0 IPS |
|
|
Text Classification (F16)
|
100% |
190
272.9 IPS |
|
|
Text Classification (I8)
|
92% |
135
194.7 IPS |
|
|
Machine Translation (F32)
|
100% |
301
5.54 IPS |
|
|
Machine Translation (F16)
|
100% |
314
5.79 IPS |
|
|
Machine Translation (I8)
|
64% |
100
1.84 IPS |
