Google octopus
Geekbench AI Score
12
Single Precision Score
11
Half Precision Score
6
Quantized Score
Geekbench AI 1.1.0 for Android AArch64
Result Information
| User | shimonocyou |
| Upload Date | October 20 2024 08:16 AM |
| Views | 3 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | placeholder |
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | Google octopus |
| Model ID | Google octopus |
| Motherboard | phaser360 |
| CPU Information | |
|---|---|
| Name | placeholder |
| Topology | 1 Processor, 4 Cores |
| Identifier | ARM implementer 78 architecture 8 variant 2 part 0 revision 1 |
| Base Frequency | 0 MHz |
| Cluster 1 | 4 Cores |
| Memory Information | |
|---|---|
| Size | 3.12 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
0% |
0
1.18 KIPS |
|
|
Image Classification (HP)
|
0% |
0
1.19 KIPS |
|
|
Image Classification (Q)
|
0% |
0
1.10 KIPS |
|
|
Image Segmentation (SP)
|
73% |
7131
123.2 IPS |
|
|
Image Segmentation (HP)
|
73% |
7018
121.3 IPS |
|
|
Image Segmentation (Q)
|
73% |
14851
256.7 IPS |
|
|
Pose Estimation (SP)
|
0% |
0
299.8 IPS |
|
|
Pose Estimation (HP)
|
0% |
0
316.3 IPS |
|
|
Pose Estimation (Q)
|
0% |
0
885.5 IPS |
|
|
Object Detection (SP)
|
0% |
0
483.3 IPS |
|
|
Object Detection (HP)
|
0% |
0
488.6 IPS |
|
|
Object Detection (Q)
|
0% |
0
347.4 IPS |
|
|
Face Detection (SP)
|
0% |
0
160.0 IPS |
|
|
Face Detection (HP)
|
0% |
0
157.8 IPS |
|
|
Face Detection (Q)
|
0% |
0
239.6 IPS |
|
|
Depth Estimation (SP)
|
0% |
0
333.9 IPS |
|
|
Depth Estimation (HP)
|
0% |
0
325.5 IPS |
|
|
Depth Estimation (Q)
|
0% |
0
292.3 IPS |
|
|
Style Transfer (SP)
|
32% |
58117
728.9 IPS |
|
|
Style Transfer (HP)
|
32% |
59444
745.6 IPS |
|
|
Style Transfer (Q)
|
0% |
0
1.80 KIPS |
|
|
Image Super-Resolution (SP)
|
1% |
0
719.4 IPS |
|
|
Image Super-Resolution (HP)
|
1% |
0
736.6 IPS |
|
|
Image Super-Resolution (Q)
|
2% |
75
879.9 IPS |
|
|
Text Classification (SP)
|
29% |
151
2.61 KIPS |
|
|
Text Classification (HP)
|
29% |
148
2.56 KIPS |
|
|
Text Classification (Q)
|
29% |
98
1.68 KIPS |
|
|
Machine Translation (SP)
|
0% |
0
3.09 IPS |
|
|
Machine Translation (HP)
|
0% |
0
3.17 IPS |
|
|
Machine Translation (Q)
|
0% |
0
2.26 IPS |
