Google octopus
Geekbench AI Score
12
Single Precision Score
12
Half Precision Score
6
Quantized Score
Geekbench AI 1.7.0 for Android AArch64
Result Information
| User | sarang_geek |
| Upload Date | March 29 2026 05:11 PM |
| Views | 9 |
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 | sparky360 |
| 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 | 6.92 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
0% |
0
1.45 KIPS |
|
|
Image Classification (HP)
|
0% |
0
1.46 KIPS |
|
|
Image Classification (Q)
|
0% |
0
1.49 KIPS |
|
|
Image Segmentation (SP)
|
73% |
7291
126.0 IPS |
|
|
Image Segmentation (HP)
|
73% |
7307
126.3 IPS |
|
|
Image Segmentation (Q)
|
73% |
15971
276.0 IPS |
|
|
Pose Estimation (SP)
|
0% |
0
358.9 IPS |
|
|
Pose Estimation (HP)
|
0% |
0
375.5 IPS |
|
|
Pose Estimation (Q)
|
0% |
0
798.9 IPS |
|
|
Object Detection (SP)
|
0% |
0
467.0 IPS |
|
|
Object Detection (HP)
|
0% |
0
409.4 IPS |
|
|
Object Detection (Q)
|
0% |
0
415.9 IPS |
|
|
Face Detection (SP)
|
0% |
0
136.3 IPS |
|
|
Face Detection (HP)
|
0% |
0
168.1 IPS |
|
|
Face Detection (Q)
|
0% |
0
281.8 IPS |
|
|
Depth Estimation (SP)
|
0% |
0
374.4 IPS |
|
|
Depth Estimation (HP)
|
0% |
0
303.3 IPS |
|
|
Depth Estimation (Q)
|
0% |
0
364.0 IPS |
|
|
Style Transfer (SP)
|
32% |
52665
660.6 IPS |
|
|
Style Transfer (HP)
|
32% |
65255
818.5 IPS |
|
|
Style Transfer (Q)
|
0% |
0
2.40 KIPS |
|
|
Image Super-Resolution (SP)
|
1% |
0
936.7 IPS |
|
|
Image Super-Resolution (HP)
|
1% |
0
936.3 IPS |
|
|
Image Super-Resolution (Q)
|
2% |
100
1.17 KIPS |
|
|
Text Classification (SP)
|
29% |
193
3.34 KIPS |
|
|
Text Classification (HP)
|
29% |
183
3.17 KIPS |
|
|
Text Classification (Q)
|
29% |
126
2.18 KIPS |
|
|
Machine Translation (SP)
|
0% |
0
3.68 IPS |
|
|
Machine Translation (HP)
|
0% |
0
3.62 IPS |
|
|
Machine Translation (Q)
|
0% |
0
2.77 IPS |
