realme RMX5079
Geekbench ML Score
197
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | June 27 2025 11:56 AM |
| Views | 18 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | realme RMX5079 |
| Model ID | realme RMX5079 |
| Motherboard | volcano |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3457 revision 1 |
| Base Frequency | 1.80 GHz |
| Cluster 1 | 4 Cores @ 1.80 GHz |
| Cluster 2 | 3 Cores @ 2.21 GHz |
| Cluster 3 | 1 Core @ 2.30 GHz |
| Memory Information | |
|---|---|
| Size | 7.24 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
92
17.2 IPS |
|
|
Image Classification (F16)
|
100% |
82
15.3 IPS |
|
|
Image Classification (I8)
|
97% |
212
39.7 IPS |
|
|
Image Segmentation (F32)
|
100% |
107
1.79 IPS |
|
|
Image Segmentation (F16)
|
100% |
113
1.89 IPS |
|
|
Image Segmentation (I8)
|
98% |
258
4.30 IPS |
|
|
Pose Estimation (F32)
|
100% |
152
0.18 IPS |
|
|
Pose Estimation (F16)
|
100% |
146
0.18 IPS |
|
|
Pose Estimation (I8)
|
100% |
535
0.65 IPS |
|
|
Object Detection (F32)
|
100% |
84
6.29 IPS |
|
|
Object Detection (F16)
|
100% |
84
6.28 IPS |
|
|
Object Detection (I8)
|
61% |
237
17.7 IPS |
|
|
Face Detection (F32)
|
100% |
288
3.43 IPS |
|
|
Face Detection (F16)
|
100% |
230
2.74 IPS |
|
|
Face Detection (I8)
|
86% |
489
5.82 IPS |
|
|
Depth Estimation (F32)
|
100% |
206
1.59 IPS |
|
|
Depth Estimation (F16)
|
100% |
192
1.49 IPS |
|
|
Depth Estimation (I8)
|
95% |
622
4.83 IPS |
|
|
Style Transfer (F32)
|
100% |
341
0.45 IPS |
|
|
Style Transfer (F16)
|
100% |
334
0.44 IPS |
|
|
Style Transfer (I8)
|
98% |
961
1.26 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
99
3.54 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
96
3.43 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
355
12.7 IPS |
|
|
Text Classification (F32)
|
100% |
109
157.4 IPS |
|
|
Text Classification (F16)
|
100% |
108
155.3 IPS |
|
|
Text Classification (I8)
|
92% |
189
271.1 IPS |
|
|
Machine Translation (F32)
|
100% |
214
3.94 IPS |
|
|
Machine Translation (F16)
|
100% |
210
3.87 IPS |
|
|
Machine Translation (I8)
|
64% |
239
4.40 IPS |
