ResNet34-SSD: Optimized for Qualcomm Devices
ResNet34-SSD is a single-stage object detection model that integrates the ResNet34 backbone with the SSD (Single Shot MultiBox Detector) framework. It is optimized for real-time detection tasks and supports multiple deployment backends including PyTorch, TensorFlow, and ONNX.
This is based on the implementation of ResNet34-SSD 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 ResNet34-SSD 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 ResNet34-SSD on GitHub for usage instructions.
Model Details
Model Type: Model_use_case.object_detection
Model Stats:
- Model checkpoint: resnet34-ssd1200
- Input resolution: 1x3x1200x1200
- Number of parameters: 20.0M
- Model size (float): 76.2 MB
Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |
|---|---|---|---|---|---|---|
| ResNet34-SSD | ONNX | float | Snapdragon® X2 Elite | 43.164 ms | 17 - 17 MB | NPU |
| ResNet34-SSD | ONNX | float | Snapdragon® X Elite | 88.605 ms | 30 - 30 MB | NPU |
| ResNet34-SSD | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 63.732 ms | 0 - 502 MB | NPU |
| ResNet34-SSD | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 176.341 ms | 17 - 447 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 86.854 ms | 17 - 21 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® QCS8450 | 176.341 ms | 17 - 447 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 135.119 ms | 16 - 36 MB | NPU |
| ResNet34-SSD | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 38.959 ms | 1 - 498 MB | NPU |
| ResNet34-SSD | ONNX | float | Snapdragon® 8 Elite Mobile | 51.894 ms | 1 - 422 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 51.894 ms | 1 - 422 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 88.605 ms | 30 - 30 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® X2 Elite | 61.754 ms | 17 - 17 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® X Elite | 129.729 ms | 17 - 17 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 84.267 ms | 0 - 587 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 260.566 ms | 3 - 509 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® QCS8275 | 481.817 ms | 16 - 384 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 127.334 ms | 17 - 366 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® SA8775P | 173.666 ms | 7 - 376 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® SA8650P | 173.666 ms | 7 - 376 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® SA8255P | 173.666 ms | 7 - 376 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® QCS8450 | 260.566 ms | 3 - 509 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 172.456 ms | 17 - 35 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 52.397 ms | 14 - 559 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® SA7255P | 481.817 ms | 16 - 384 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 67.113 ms | 9 - 385 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® SA8295P | 183.497 ms | 0 - 329 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 67.113 ms | 9 - 385 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 129.729 ms | 17 - 17 MB | NPU |
| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 107.865 ms | 0 - 546 MB | NPU |
| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 229.355 ms | 1 - 617 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® QCS8275 | 512.823 ms | 0 - 378 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 146.413 ms | 0 - 4 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® SA8775P | 183.677 ms | 0 - 426 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® SA8650P | 183.677 ms | 0 - 426 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® SA8255P | 183.677 ms | 0 - 426 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® QCS8450 | 229.355 ms | 1 - 617 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 181.892 ms | 0 - 65 MB | NPU |
| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 72.174 ms | 0 - 570 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® SA7255P | 512.823 ms | 0 - 378 MB | NPU |
| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Elite Mobile | 88.236 ms | 0 - 403 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® SA8295P | 202.138 ms | 0 - 354 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 88.236 ms | 0 - 403 MB | NPU |
License
- The license for the original implementation of ResNet34-SSD 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.
