Hugging Face's logo

qualcomm
/
ResNet-Mixed-Convolution

Video Classification
PyTorch
backbone
android
Model card Files
xet
Community
1
ResNet-Mixed-Convolution
File size: 9,681 Bytes
1cbfac7
 
76be976
1cbfac7
 
 
4b6cff5
1cbfac7
 
 
 
 
7be4d2d
1cbfac7
 
 
7be4d2d
529bc4a
7be4d2d
 
 
 
 
 
 
 
 
 
 
 
529bc4a
 
 
 
 
7be4d2d
 
 
 
 
 
529bc4a
7be4d2d
 
 
 
 
 
529bc4a
7be4d2d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
529bc4a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1cbfac7
 
a6d3324
 
1cbfac7
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
 
---
library_name: pytorch
license: other
tags:
- backbone
- android
pipeline_tag: video-classification

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_mixed/web-assets/model_demo.png)

# ResNet-Mixed-Convolution: Optimized for Qualcomm Devices

ResNet Mixed Convolutions is a network with a mixture of 2D and 3D convolutions used for video understanding.

This is based on the implementation of ResNet-Mixed-Convolution found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/resnet_mixed) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.

## Getting Started
There are two ways to deploy this model on your device:

### Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_mixed/releases/v0.48.0/resnet_mixed-onnx-float.zip)
| ONNX | w8a16 | Universal | QAIRT 2.42, ONNX Runtime 1.24.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_mixed/releases/v0.48.0/resnet_mixed-onnx-w8a16.zip)
| QNN_DLC | float | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_mixed/releases/v0.48.0/resnet_mixed-qnn_dlc-float.zip)
| QNN_DLC | w8a16 | Universal | QAIRT 2.43 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_mixed/releases/v0.48.0/resnet_mixed-qnn_dlc-w8a16.zip)
| TFLITE | float | Universal | QAIRT 2.43, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_mixed/releases/v0.48.0/resnet_mixed-tflite-float.zip)

For more device-specific assets and performance metrics, visit **[ResNet-Mixed-Convolution on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/resnet_mixed)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/resnet_mixed) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for [ResNet-Mixed-Convolution on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/qai_hub_models/models/resnet_mixed) for usage instructions.

## Model Details

**Model Type:** Model_use_case.video_classification

**Model Stats:**
- Model checkpoint: Kinetics-400
- Input resolution: 112x112
- Number of parameters: 11.7M
- Model size (float): 44.6 MB
- Model size (w8a16): 11.5 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| ResNet-Mixed-Convolution | ONNX | float | Snapdragon® X2 Elite | 7.099 ms | 22 - 22 MB | NPU
| ResNet-Mixed-Convolution | ONNX | float | Snapdragon® X Elite | 13.823 ms | 22 - 22 MB | NPU
| ResNet-Mixed-Convolution | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 9.611 ms | 2 - 276 MB | NPU
| ResNet-Mixed-Convolution | ONNX | float | Qualcomm® QCS8550 (Proxy) | 13.288 ms | 0 - 29 MB | NPU
| ResNet-Mixed-Convolution | ONNX | float | Qualcomm® QCS9075 | 26.624 ms | 2 - 5 MB | NPU
| ResNet-Mixed-Convolution | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.811 ms | 0 - 215 MB | NPU
| ResNet-Mixed-Convolution | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.559 ms | 0 - 219 MB | NPU
| ResNet-Mixed-Convolution | ONNX | w8a16 | Snapdragon® X2 Elite | 4.647 ms | 12 - 12 MB | NPU
| ResNet-Mixed-Convolution | ONNX | w8a16 | Snapdragon® X Elite | 9.09 ms | 11 - 11 MB | NPU
| ResNet-Mixed-Convolution | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 6.392 ms | 1 - 252 MB | NPU
| ResNet-Mixed-Convolution | ONNX | w8a16 | Qualcomm® QCS6490 | 1727.214 ms | 51 - 62 MB | CPU
| ResNet-Mixed-Convolution | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 8.515 ms | 1 - 5 MB | NPU
| ResNet-Mixed-Convolution | ONNX | w8a16 | Qualcomm® QCS9075 | 9.054 ms | 1 - 4 MB | NPU
| ResNet-Mixed-Convolution | ONNX | w8a16 | Qualcomm® QCM6690 | 890.657 ms | 107 - 114 MB | CPU
| ResNet-Mixed-Convolution | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 5.285 ms | 1 - 193 MB | NPU
| ResNet-Mixed-Convolution | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 874.466 ms | 112 - 120 MB | CPU
| ResNet-Mixed-Convolution | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 3.516 ms | 0 - 190 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Snapdragon® X2 Elite | 7.652 ms | 2 - 2 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Snapdragon® X Elite | 13.982 ms | 2 - 2 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 9.56 ms | 0 - 284 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 97.146 ms | 1 - 223 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 13.22 ms | 2 - 34 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Qualcomm® SA8775P | 25.075 ms | 1 - 222 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Qualcomm® QCS9075 | 27.603 ms | 2 - 6 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 27.484 ms | 2 - 244 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Qualcomm® SA7255P | 97.146 ms | 1 - 223 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Qualcomm® SA8295P | 26.782 ms | 0 - 194 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.629 ms | 0 - 225 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.732 ms | 2 - 233 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 5.359 ms | 1 - 1 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Snapdragon® X Elite | 9.823 ms | 1 - 1 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 6.624 ms | 1 - 254 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 37.274 ms | 0 - 3 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 29.75 ms | 1 - 190 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 9.172 ms | 1 - 3 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Qualcomm® SA8775P | 9.393 ms | 1 - 191 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 10.391 ms | 1 - 4 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 182.729 ms | 1 - 208 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 14.0 ms | 1 - 255 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Qualcomm® SA7255P | 29.75 ms | 1 - 190 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Qualcomm® SA8295P | 16.32 ms | 1 - 192 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 5.267 ms | 1 - 187 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 15.899 ms | 1 - 197 MB | NPU
| ResNet-Mixed-Convolution | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 4.005 ms | 7 - 195 MB | NPU
| ResNet-Mixed-Convolution | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 217.382 ms | 0 - 312 MB | NPU
| ResNet-Mixed-Convolution | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 584.652 ms | 0 - 249 MB | NPU
| ResNet-Mixed-Convolution | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 304.587 ms | 0 - 2 MB | NPU
| ResNet-Mixed-Convolution | TFLITE | float | Qualcomm® SA8775P | 294.794 ms | 0 - 249 MB | NPU
| ResNet-Mixed-Convolution | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 339.962 ms | 0 - 278 MB | NPU
| ResNet-Mixed-Convolution | TFLITE | float | Qualcomm® SA7255P | 584.652 ms | 0 - 249 MB | NPU
| ResNet-Mixed-Convolution | TFLITE | float | Qualcomm® SA8295P | 363.01 ms | 0 - 232 MB | NPU
| ResNet-Mixed-Convolution | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 201.863 ms | 0 - 246 MB | NPU
| ResNet-Mixed-Convolution | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 198.096 ms | 0 - 251 MB | NPU

## License
* The license for the original implementation of ResNet-Mixed-Convolution can be found
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).

## References
* [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://arxiv.org/abs/1711.11248)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py)

## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).