ResNeXt101: Optimized for Qualcomm Devices
ResNeXt101 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 ResNeXt101 found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.
Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up 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 1.24.3 | Download |
| ONNX | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | Download |
| QNN_DLC | float | Universal | QAIRT 2.45 | Download |
| QNN_DLC | w8a8 | Universal | QAIRT 2.45 | Download |
| TFLITE | float | Universal | QAIRT 2.45 | Download |
| TFLITE | w8a8 | Universal | QAIRT 2.45 | Download |
For more device-specific assets and performance metrics, visit ResNeXt101 on Qualcomm® AI Hub.
Option 2: Export with Custom Configurations
Use the Qualcomm® AI Hub Models 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 ResNeXt101 on GitHub for usage instructions.
Model Details
Model Type: Model_use_case.image_classification
Model Stats:
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 88.7M
- Model size (float): 338 MB
- Model size (w8a8): 87.3 MB
Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |
|---|---|---|---|---|---|---|
| ResNeXt101 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.047 ms | 1 - 203 MB | NPU |
| ResNeXt101 | ONNX | float | Snapdragon® X2 Elite | 3.095 ms | 173 - 173 MB | NPU |
| ResNeXt101 | ONNX | float | Snapdragon® X Elite | 6.716 ms | 172 - 172 MB | NPU |
| ResNeXt101 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 4.525 ms | 0 - 386 MB | NPU |
| ResNeXt101 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 6.736 ms | 0 - 196 MB | NPU |
| ResNeXt101 | ONNX | float | Qualcomm® QCS9075 | 9.763 ms | 0 - 4 MB | NPU |
| ResNeXt101 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.935 ms | 0 - 212 MB | NPU |
| ResNeXt101 | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 1.573 ms | 0 - 217 MB | NPU |
| ResNeXt101 | ONNX | w8a8 | Snapdragon® X2 Elite | 1.388 ms | 87 - 87 MB | NPU |
| ResNeXt101 | ONNX | w8a8 | Snapdragon® X Elite | 3.128 ms | 87 - 87 MB | NPU |
| ResNeXt101 | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 2.148 ms | 0 - 254 MB | NPU |
| ResNeXt101 | ONNX | w8a8 | Qualcomm® QCS6490 | 112.859 ms | 12 - 44 MB | CPU |
| ResNeXt101 | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 2.993 ms | 0 - 99 MB | NPU |
| ResNeXt101 | ONNX | w8a8 | Qualcomm® QCS9075 | 3.133 ms | 0 - 3 MB | NPU |
| ResNeXt101 | ONNX | w8a8 | Qualcomm® QCM6690 | 73.566 ms | 1 - 13 MB | CPU |
| ResNeXt101 | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 1.891 ms | 0 - 218 MB | NPU |
| ResNeXt101 | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 69.187 ms | 0 - 12 MB | CPU |
| ResNeXt101 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.14 ms | 0 - 189 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Snapdragon® X2 Elite | 3.646 ms | 1 - 1 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Snapdragon® X Elite | 6.916 ms | 1 - 1 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 4.685 ms | 1 - 368 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 35.98 ms | 1 - 181 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 6.738 ms | 1 - 5 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Qualcomm® SA8775P | 10.3 ms | 1 - 195 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Qualcomm® QCS9075 | 9.97 ms | 1 - 3 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 9.942 ms | 0 - 309 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Qualcomm® SA7255P | 35.98 ms | 1 - 181 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Qualcomm® SA8295P | 10.912 ms | 1 - 134 MB | NPU |
| ResNeXt101 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.961 ms | 0 - 181 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 1.496 ms | 0 - 214 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 1.589 ms | 0 - 0 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Snapdragon® X Elite | 3.045 ms | 0 - 0 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 2.108 ms | 60 - 318 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 9.27 ms | 2 - 4 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 6.595 ms | 0 - 210 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 2.917 ms | 0 - 2 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 3.496 ms | 0 - 213 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 3.11 ms | 0 - 2 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 32.518 ms | 0 - 375 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 3.979 ms | 0 - 260 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 6.595 ms | 0 - 210 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 4.243 ms | 0 - 215 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 1.808 ms | 0 - 210 MB | NPU |
| ResNeXt101 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 4.051 ms | 0 - 242 MB | NPU |
| ResNeXt101 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.096 ms | 0 - 364 MB | NPU |
| ResNeXt101 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 4.586 ms | 0 - 528 MB | NPU |
| ResNeXt101 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 35.986 ms | 0 - 359 MB | NPU |
| ResNeXt101 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 6.842 ms | 0 - 3 MB | NPU |
| ResNeXt101 | TFLITE | float | Qualcomm® SA8775P | 10.224 ms | 0 - 358 MB | NPU |
| ResNeXt101 | TFLITE | float | Qualcomm® QCS9075 | 10.023 ms | 0 - 174 MB | NPU |
| ResNeXt101 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 9.861 ms | 0 - 477 MB | NPU |
| ResNeXt101 | TFLITE | float | Qualcomm® SA7255P | 35.986 ms | 0 - 359 MB | NPU |
| ResNeXt101 | TFLITE | float | Qualcomm® SA8295P | 10.973 ms | 0 - 304 MB | NPU |
| ResNeXt101 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.986 ms | 0 - 360 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 1.453 ms | 0 - 213 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 1.968 ms | 0 - 257 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Qualcomm® QCS6490 | 8.898 ms | 0 - 88 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 6.265 ms | 0 - 213 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 2.736 ms | 0 - 4 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Qualcomm® SA8775P | 3.313 ms | 0 - 214 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Qualcomm® QCS9075 | 2.92 ms | 0 - 89 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Qualcomm® QCM6690 | 26.78 ms | 0 - 375 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 3.806 ms | 0 - 255 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Qualcomm® SA7255P | 6.265 ms | 0 - 213 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Qualcomm® SA8295P | 4.094 ms | 0 - 216 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 1.736 ms | 0 - 212 MB | NPU |
| ResNeXt101 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 3.94 ms | 0 - 243 MB | NPU |
License
- The license for the original implementation of ResNeXt101 can be found here.
References
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.
