BLU B160V
Geekbench ML Score
129
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 04 2024 05:33 AM |
| Views | 592 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | BLU B160V |
| Model ID | BLU B160V |
| Motherboard | B160V |
| CPU Information | |
|---|---|
| Name | ARM MT6761 |
| 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 | 2.75 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
67
12.6 IPS |
|
|
Image Classification (F16)
|
100% |
68
12.7 IPS |
|
|
Image Classification (I8)
|
97% |
104
19.4 IPS |
|
|
Image Segmentation (F32)
|
100% |
93
1.55 IPS |
|
|
Image Segmentation (F16)
|
100% |
93
1.55 IPS |
|
|
Image Segmentation (I8)
|
98% |
114
1.91 IPS |
|
|
Pose Estimation (F32)
|
100% |
114
0.14 IPS |
|
|
Pose Estimation (F16)
|
100% |
115
0.14 IPS |
|
|
Pose Estimation (I8)
|
100% |
270
0.33 IPS |
|
|
Object Detection (F32)
|
100% |
72
5.37 IPS |
|
|
Object Detection (F16)
|
100% |
71
5.27 IPS |
|
|
Object Detection (I8)
|
61% |
113
8.44 IPS |
|
|
Face Detection (F32)
|
100% |
159
1.89 IPS |
|
|
Face Detection (F16)
|
100% |
158
1.88 IPS |
|
|
Face Detection (I8)
|
86% |
280
3.33 IPS |
|
|
Depth Estimation (F32)
|
100% |
145
1.12 IPS |
|
|
Depth Estimation (F16)
|
100% |
145
1.13 IPS |
|
|
Depth Estimation (I8)
|
95% |
256
1.99 IPS |
|
|
Style Transfer (F32)
|
100% |
236
0.31 IPS |
|
|
Style Transfer (F16)
|
100% |
241
0.32 IPS |
|
|
Style Transfer (I8)
|
98% |
525
0.69 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
84
3.00 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
85
3.02 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
168
6.02 IPS |
|
|
Text Classification (F32)
|
100% |
86
123.0 IPS |
|
|
Text Classification (F16)
|
100% |
86
123.9 IPS |
|
|
Text Classification (I8)
|
92% |
87
125.6 IPS |
|
|
Machine Translation (F32)
|
100% |
163
3.01 IPS |
|
|
Machine Translation (F16)
|
100% |
160
2.94 IPS |
|
|
Machine Translation (I8)
|
64% |
103
1.90 IPS |
