Punkt MC02
Geekbench ML Score
279
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 21 2024 09:52 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | Punkt MC02 |
| Model ID | Punkt MC02 |
| Motherboard | k6877v1_64_k419 |
| CPU Information | |
|---|---|
| Name | ARM MT6877V/ZA |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3393 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.40 GHz |
| Memory Information | |
|---|---|
| Size | 5.54 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
158
29.6 IPS |
|
|
Image Classification (F16)
|
100% |
287
53.8 IPS |
|
|
Image Classification (I8)
|
97% |
261
48.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
214
3.57 IPS |
|
|
Image Segmentation (F16)
|
100% |
342
5.71 IPS |
|
|
Image Segmentation (I8)
|
98% |
331
5.53 IPS |
|
|
Pose Estimation (F32)
|
100% |
148
0.18 IPS |
|
|
Pose Estimation (F16)
|
100% |
173
0.21 IPS |
|
|
Pose Estimation (I8)
|
100% |
173
0.21 IPS |
|
|
Object Detection (F32)
|
100% |
123
9.15 IPS |
|
|
Object Detection (F16)
|
100% |
187
14.0 IPS |
|
|
Object Detection (I8)
|
62% |
179
13.4 IPS |
|
|
Face Detection (F32)
|
100% |
517
6.15 IPS |
|
|
Face Detection (F16)
|
98% |
710
8.45 IPS |
|
|
Face Detection (I8)
|
86% |
740
8.79 IPS |
|
|
Depth Estimation (F32)
|
100% |
291
2.26 IPS |
|
|
Depth Estimation (F16)
|
100% |
376
2.92 IPS |
|
|
Depth Estimation (I8)
|
95% |
369
2.86 IPS |
|
|
Style Transfer (F32)
|
100% |
582
0.77 IPS |
|
|
Style Transfer (F16)
|
100% |
819
1.08 IPS |
|
|
Style Transfer (I8)
|
98% |
818
1.08 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
157
5.59 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
243
8.68 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
240
8.56 IPS |
|
|
Text Classification (F32)
|
100% |
145
208.8 IPS |
|
|
Text Classification (F16)
|
100% |
144
206.6 IPS |
|
|
Text Classification (I8)
|
92% |
311
446.5 IPS |
|
|
Machine Translation (F32)
|
100% |
241
4.43 IPS |
|
|
Machine Translation (F16)
|
100% |
243
4.46 IPS |
|
|
Machine Translation (I8)
|
64% |
314
5.79 IPS |
