Hugging Face's logo

qualcomm
/
ResNeXt50

Image Classification
PyTorch
backbone
bu_auto
android
Model card Files
xet
Community
ResNeXt50
File size: 9,771 Bytes 
1ca3652
 
dc9ebd3
1ca3652
 
5c3ad4e
1ca3652
a7ea068
1ca3652
 
 
e70eebf
1ca3652
f5579c2
e882320
1ca3652
 
f5579c2
 
 
 
 
 
 
 
 
 
 
 
 
 
ce2cc73
 
 
 
 
 
f5579c2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
aa829d1
1ca3652
98c2ea2
 
aa829d1
1ca3652
 
 
 
 
66f1506
1ca3652
 
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
131
132
133
134
135
136
137
138
139
140
141
142
 
---
library_name: pytorch
license: other
tags:
- backbone
- bu_auto
- android
pipeline_tag: image-classification

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnext50/web-assets/model_demo.png)

# ResNeXt50: Optimized for Qualcomm Devices

ResNeXt50 is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.

This is based on the implementation of ResNeXt50 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/resnext50) 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 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnext50/releases/v0.46.0/resnext50-onnx-float.zip)
| ONNX | w8a8 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnext50/releases/v0.46.0/resnext50-onnx-w8a8.zip)
| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnext50/releases/v0.46.0/resnext50-qnn_dlc-float.zip)
| QNN_DLC | w8a8 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnext50/releases/v0.46.0/resnext50-qnn_dlc-w8a8.zip)
| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnext50/releases/v0.46.0/resnext50-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnext50/releases/v0.46.0/resnext50-tflite-w8a8.zip)

For more device-specific assets and performance metrics, visit **[ResNeXt50 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/resnext50)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/resnext50) 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 [ResNeXt50 on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/resnext50) for usage instructions.

## Model Details

**Model Type:** Model_use_case.image_classification

**Model Stats:**
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 25.0M
- Model size (float): 95.4 MB
- Model size (w8a8): 24.8 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| ResNeXt50 | ONNX | float | Snapdragon® X Elite | 2.448 ms | 50 - 50 MB | NPU
| ResNeXt50 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 1.894 ms | 0 - 194 MB | NPU
| ResNeXt50 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 2.573 ms | 0 - 311 MB | NPU
| ResNeXt50 | ONNX | float | Qualcomm® QCS9075 | 3.68 ms | 1 - 4 MB | NPU
| ResNeXt50 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.569 ms | 0 - 133 MB | NPU
| ResNeXt50 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.31 ms | 0 - 135 MB | NPU
| ResNeXt50 | ONNX | w8a8 | Snapdragon® X Elite | 1.236 ms | 26 - 26 MB | NPU
| ResNeXt50 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 1.051 ms | 0 - 168 MB | NPU
| ResNeXt50 | ONNX | w8a8 | Qualcomm® QCS6490 | 47.343 ms | 7 - 29 MB | CPU
| ResNeXt50 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 1.345 ms | 0 - 30 MB | NPU
| ResNeXt50 | ONNX | w8a8 | Qualcomm® QCS9075 | 1.483 ms | 0 - 3 MB | NPU
| ResNeXt50 | ONNX | w8a8 | Qualcomm® QCM6690 | 26.039 ms | 0 - 9 MB | CPU
| ResNeXt50 | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.854 ms | 0 - 142 MB | NPU
| ResNeXt50 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 20.332 ms | 0 - 10 MB | CPU
| ResNeXt50 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.747 ms | 0 - 141 MB | NPU
| ResNeXt50 | QNN_DLC | float | Snapdragon® X Elite | 2.671 ms | 1 - 1 MB | NPU
| ResNeXt50 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1.824 ms | 0 - 138 MB | NPU
| ResNeXt50 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 11.971 ms | 1 - 76 MB | NPU
| ResNeXt50 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 2.483 ms | 1 - 2 MB | NPU
| ResNeXt50 | QNN_DLC | float | Qualcomm® SA8775P | 3.835 ms | 1 - 76 MB | NPU
| ResNeXt50 | QNN_DLC | float | Qualcomm® QCS9075 | 3.636 ms | 1 - 3 MB | NPU
| ResNeXt50 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 3.963 ms | 0 - 114 MB | NPU
| ResNeXt50 | QNN_DLC | float | Qualcomm® SA7255P | 11.971 ms | 1 - 76 MB | NPU
| ResNeXt50 | QNN_DLC | float | Qualcomm® SA8295P | 4.187 ms | 0 - 53 MB | NPU
| ResNeXt50 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.483 ms | 0 - 75 MB | NPU
| ResNeXt50 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.204 ms | 1 - 77 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Snapdragon® X Elite | 1.26 ms | 0 - 0 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.808 ms | 0 - 97 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 3.097 ms | 0 - 2 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 2.497 ms | 0 - 69 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 1.093 ms | 0 - 2 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 1.453 ms | 0 - 70 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 1.216 ms | 2 - 4 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 7.668 ms | 0 - 197 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 1.442 ms | 0 - 98 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 2.497 ms | 0 - 69 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 1.766 ms | 0 - 66 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.633 ms | 0 - 71 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.395 ms | 0 - 74 MB | NPU
| ResNeXt50 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.523 ms | 0 - 72 MB | NPU
| ResNeXt50 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 1.784 ms | 0 - 187 MB | NPU
| ResNeXt50 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 12.016 ms | 0 - 129 MB | NPU
| ResNeXt50 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 2.455 ms | 0 - 3 MB | NPU
| ResNeXt50 | TFLITE | float | Qualcomm® SA8775P | 3.818 ms | 0 - 128 MB | NPU
| ResNeXt50 | TFLITE | float | Qualcomm® QCS9075 | 3.684 ms | 0 - 52 MB | NPU
| ResNeXt50 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 4.056 ms | 0 - 162 MB | NPU
| ResNeXt50 | TFLITE | float | Qualcomm® SA7255P | 12.016 ms | 0 - 129 MB | NPU
| ResNeXt50 | TFLITE | float | Qualcomm® SA8295P | 4.211 ms | 0 - 104 MB | NPU
| ResNeXt50 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.452 ms | 0 - 129 MB | NPU
| ResNeXt50 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 1.201 ms | 0 - 129 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.674 ms | 0 - 97 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Qualcomm® QCS6490 | 2.876 ms | 0 - 27 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 2.196 ms | 0 - 68 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.915 ms | 0 - 3 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Qualcomm® SA8775P | 4.4 ms | 0 - 69 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Qualcomm® QCS9075 | 1.017 ms | 0 - 27 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Qualcomm® QCM6690 | 7.322 ms | 0 - 190 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 1.276 ms | 0 - 99 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Qualcomm® SA7255P | 2.196 ms | 0 - 68 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Qualcomm® SA8295P | 1.558 ms | 0 - 62 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.557 ms | 0 - 73 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1.239 ms | 0 - 66 MB | NPU
| ResNeXt50 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.48 ms | 0 - 71 MB | NPU

## License
* The license for the original implementation of ResNeXt50 can be found
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).

## References
* [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/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).