Realme GT Neo
Geekbench ML Score
542
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | April 13 2024 08:31 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | Realme GT Neo |
| Model ID | realme RMX3031 |
| Motherboard | RM6893 |
| CPU Information | |
|---|---|
| Name | ARM MT6893Z/CZA |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3393 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 4 Cores @ 2.00 GHz |
| Cluster 2 | 3 Cores @ 2.60 GHz |
| Cluster 3 | 1 Core @ 3.00 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% |
195
36.5 IPS |
|
|
Image Classification (F16)
|
100% |
283
52.9 IPS |
|
|
Image Classification (I8)
|
97% |
673
125.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
355
5.94 IPS |
|
|
Image Segmentation (F16)
|
100% |
346
5.78 IPS |
|
|
Image Segmentation (I8)
|
98% |
505
8.44 IPS |
|
|
Pose Estimation (F32)
|
100% |
616
0.75 IPS |
|
|
Pose Estimation (F16)
|
100% |
582
0.71 IPS |
|
|
Pose Estimation (I8)
|
100% |
2074
2.51 IPS |
|
|
Object Detection (F32)
|
100% |
265
19.8 IPS |
|
|
Object Detection (F16)
|
100% |
265
19.8 IPS |
|
|
Object Detection (I8)
|
61% |
683
51.0 IPS |
|
|
Face Detection (F32)
|
100% |
569
6.76 IPS |
|
|
Face Detection (F16)
|
100% |
566
6.73 IPS |
|
|
Face Detection (I8)
|
86% |
819
9.73 IPS |
|
|
Depth Estimation (F32)
|
100% |
511
3.96 IPS |
|
|
Depth Estimation (F16)
|
100% |
523
4.06 IPS |
|
|
Depth Estimation (I8)
|
95% |
888
6.89 IPS |
|
|
Style Transfer (F32)
|
100% |
1106
1.45 IPS |
|
|
Style Transfer (F16)
|
100% |
1121
1.48 IPS |
|
|
Style Transfer (I8)
|
98% |
2732
3.59 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
238
8.49 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
398
14.2 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1237
44.2 IPS |
|
|
Text Classification (F32)
|
100% |
313
449.2 IPS |
|
|
Text Classification (F16)
|
100% |
338
485.3 IPS |
|
|
Text Classification (I8)
|
92% |
372
534.3 IPS |
|
|
Machine Translation (F32)
|
100% |
691
12.7 IPS |
|
|
Machine Translation (F16)
|
100% |
740
13.6 IPS |
|
|
Machine Translation (I8)
|
64% |
267
4.91 IPS |
