KEYENCE DX-A600
Geekbench ML Score
13
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 09 2024 08:06 AM |
| Views | 3 |
System Information
| System Information | |
|---|---|
| Operating System | Android 12 |
| Model | KEYENCE DX-A600 |
| Model ID | KEYENCE DX-A600 |
| Motherboard | DX-A600_ |
| CPU Information | |
|---|---|
| Name | ARM MT6762 |
| Topology | 1 Processor, 4 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3331 revision 4 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 4 Cores @ 2.00 GHz |
| Memory Information | |
|---|---|
| Size | 3.75 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
14
2.55 IPS |
|
|
Image Classification (F16)
|
100% |
20
3.81 IPS |
|
|
Image Classification (I8)
|
99% |
20
3.69 IPS |
|
|
Image Segmentation (F32)
|
100% |
22
0.36 IPS |
|
|
Image Segmentation (F16)
|
100% |
27
0.44 IPS |
|
|
Image Segmentation (I8)
|
98% |
26
0.44 IPS |
|
|
Pose Estimation (F32)
|
0
|
||
|
Pose Estimation (F16)
|
0
|
||
|
Pose Estimation (I8)
|
0
|
||
|
Object Detection (F32)
|
100% |
11
0.79 IPS |
|
|
Object Detection (F16)
|
100% |
15
1.14 IPS |
|
|
Object Detection (I8)
|
55% |
15
1.13 IPS |
|
|
Face Detection (F32)
|
100% |
42
0.50 IPS |
|
|
Face Detection (F16)
|
98% |
56
0.66 IPS |
|
|
Face Detection (I8)
|
86% |
53
0.63 IPS |
|
|
Depth Estimation (F32)
|
100% |
26
0.21 IPS |
|
|
Depth Estimation (F16)
|
100% |
42
0.32 IPS |
|
|
Depth Estimation (I8)
|
93% |
40
0.31 IPS |
|
|
Style Transfer (F32)
|
0
|
||
|
Style Transfer (F16)
|
0
|
||
|
Style Transfer (I8)
|
0
|
||
|
Image Super-Resolution (F32)
|
100% |
12
0.43 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
21
0.73 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
19
0.68 IPS |
|
|
Text Classification (F32)
|
100% |
25
35.7 IPS |
|
|
Text Classification (F16)
|
100% |
27
38.6 IPS |
|
|
Text Classification (I8)
|
92% |
41
58.7 IPS |
|
|
Machine Translation (F32)
|
100% |
47
0.87 IPS |
|
|
Machine Translation (F16)
|
100% |
47
0.86 IPS |
|
|
Machine Translation (I8)
|
64% |
55
1.01 IPS |
