Xiaomi Redmi Note 7
Geekbench AI Score
76
Single Precision Score
76
Half Precision Score
115
Quantized Score
Geekbench AI 1.2.0 for Android AArch64
Result Information
| Upload Date | June 22 2025 09:47 AM |
| Views | 1 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Qualcomm Snapdragon 660 |
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | Xiaomi Redmi Note 7 |
| Model ID | Xiaomi Redmi Note 7 |
| Motherboard | sdm660 |
| Governor | schedutil |
| CPU Information | |
|---|---|
| Name | Qualcomm Qualcomm |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 81 architecture 8 variant 10 part 2048 revision 2 |
| Base Frequency | 1.84 GHz |
| Cluster 1 | 4 Cores @ 1.84 GHz |
| Cluster 2 | 4 Cores @ 2.21 GHz |
| Memory Information | |
|---|---|
| Size | 3.58 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
43
7.96 IPS |
|
|
Image Classification (HP)
|
100% |
44
8.10 IPS |
|
|
Image Classification (Q)
|
99% |
74
13.7 IPS |
|
|
Image Segmentation (SP)
|
100% |
59
0.95 IPS |
|
|
Image Segmentation (HP)
|
100% |
60
0.97 IPS |
|
|
Image Segmentation (Q)
|
98% |
83
1.35 IPS |
|
|
Pose Estimation (SP)
|
100% |
84
0.10 IPS |
|
|
Pose Estimation (HP)
|
100% |
83
0.10 IPS |
|
|
Pose Estimation (Q)
|
98% |
160
0.19 IPS |
|
|
Object Detection (SP)
|
100% |
44
3.46 IPS |
|
|
Object Detection (HP)
|
100% |
44
3.48 IPS |
|
|
Object Detection (Q)
|
87% |
75
6.00 IPS |
|
|
Face Detection (SP)
|
100% |
111
1.32 IPS |
|
|
Face Detection (HP)
|
100% |
113
1.35 IPS |
|
|
Face Detection (Q)
|
97% |
178
2.12 IPS |
|
|
Depth Estimation (SP)
|
100% |
101
0.78 IPS |
|
|
Depth Estimation (HP)
|
99% |
101
0.78 IPS |
|
|
Depth Estimation (Q)
|
64% |
148
1.34 IPS |
|
|
Style Transfer (SP)
|
100% |
188
0.24 IPS |
|
|
Style Transfer (HP)
|
100% |
188
0.24 IPS |
|
|
Style Transfer (Q)
|
98% |
359
0.46 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
49
1.82 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
49
1.82 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
93
3.44 IPS |
|
|
Text Classification (SP)
|
100% |
63
83.5 IPS |
|
|
Text Classification (HP)
|
100% |
61
81.0 IPS |
|
|
Text Classification (Q)
|
91% |
82
110.0 IPS |
|
|
Machine Translation (SP)
|
100% |
107
1.84 IPS |
|
|
Machine Translation (HP)
|
100% |
110
1.89 IPS |
|
|
Machine Translation (Q)
|
57% |
78
1.91 IPS |
