DETR-ResNet101-DC5: Optimized for Qualcomm Devices
DETR is a machine learning model that can detect objects (trained on COCO dataset).
This is based on the implementation of DETR-ResNet101-DC5 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 2.45, ONNX Runtime 1.25.0 | Download |
| QNN_DLC | float | Universal | QAIRT 2.45 | Download |
| TFLITE | float | Universal | QAIRT 2.45 | Download |
For more device-specific assets and performance metrics, visit DETR-ResNet101-DC5 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 DETR-ResNet101-DC5 on GitHub for usage instructions.
Model Details
Model Type: Model_use_case.object_detection
Model Stats:
- Model checkpoint: ResNet101-DC5
- Input resolution: 480x480
- Model size (float): 232 MB
Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |
|---|---|---|---|---|---|---|
| DETR-ResNet101-DC5 | ONNX | float | Snapdragon® X2 Elite | 23.54 ms | 5 - 5 MB | NPU |
| DETR-ResNet101-DC5 | ONNX | float | Snapdragon® X Elite | 51.268 ms | 115 - 115 MB | NPU |
| DETR-ResNet101-DC5 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 36.513 ms | 1 - 570 MB | NPU |
| DETR-ResNet101-DC5 | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 84.694 ms | 2 - 478 MB | NPU |
| DETR-ResNet101-DC5 | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 49.694 ms | 0 - 126 MB | NPU |
| DETR-ResNet101-DC5 | ONNX | float | Qualcomm® QCS8450 | 84.694 ms | 2 - 478 MB | NPU |
| DETR-ResNet101-DC5 | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 69.888 ms | 5 - 12 MB | NPU |
| DETR-ResNet101-DC5 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 21.756 ms | 1 - 442 MB | NPU |
| DETR-ResNet101-DC5 | ONNX | float | Snapdragon® 8 Elite Mobile | 27.242 ms | 0 - 431 MB | NPU |
| DETR-ResNet101-DC5 | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 27.242 ms | 0 - 431 MB | NPU |
| DETR-ResNet101-DC5 | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 51.268 ms | 115 - 115 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Snapdragon® X2 Elite | 24.92 ms | 5 - 5 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Snapdragon® X Elite | 53.405 ms | 5 - 5 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 37.453 ms | 3 - 554 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 109.013 ms | 5 - 469 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® QCS8275 | 220.26 ms | 1 - 426 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 51.237 ms | 5 - 282 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® SA8775P | 69.802 ms | 1 - 427 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® SA8650P | 69.802 ms | 1 - 427 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® SA8255P | 69.802 ms | 1 - 427 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® QCS8450 | 109.013 ms | 5 - 469 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 70.381 ms | 5 - 11 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 22.634 ms | 5 - 451 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® SA7255P | 220.26 ms | 1 - 426 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 28.191 ms | 0 - 449 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® SA8295P | 86.106 ms | 0 - 341 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 28.191 ms | 0 - 449 MB | NPU |
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 53.405 ms | 5 - 5 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 37.318 ms | 0 - 602 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 84.45 ms | 0 - 481 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Qualcomm® QCS8275 | 219.433 ms | 0 - 471 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 50.736 ms | 0 - 3 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Qualcomm® SA8775P | 69.58 ms | 0 - 470 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Qualcomm® SA8650P | 69.58 ms | 0 - 470 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Qualcomm® SA8255P | 69.58 ms | 0 - 470 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Qualcomm® QCS8450 | 84.45 ms | 0 - 481 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 69.323 ms | 0 - 126 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 22.257 ms | 0 - 495 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Qualcomm® SA7255P | 219.433 ms | 0 - 471 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Snapdragon® 8 Elite Mobile | 28.028 ms | 0 - 478 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Qualcomm® SA8295P | 76.616 ms | 0 - 358 MB | NPU |
| DETR-ResNet101-DC5 | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 28.028 ms | 0 - 478 MB | NPU |
License
- The license for the original implementation of DETR-ResNet101-DC5 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.
