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 2.50, ONNX Runtime 1.30.0 | Download |
| QNN_DLC | float | Universal | QAIRT 2.50 | Download |
| TFLITE | float | Universal | QAIRT 2.50 | 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:
- Input resolution: 1x3x1200x1200
- Model checkpoint: resnet34-ssd1200
- Model size (float): 76.2 MB
- Number of parameters: 20.0M
Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |
|---|---|---|---|---|---|---|
| ResNet34-SSD | ONNX | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 32.165 ms | 1 - 277 MB | NPU |
| ResNet34-SSD | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 42.685 ms | 1 - 250 MB | NPU |
| ResNet34-SSD | ONNX | float | Snapdragon® X2 Elite | 40.125 ms | 17 - 17 MB | NPU |
| ResNet34-SSD | ONNX | float | Snapdragon® X Elite | 68.228 ms | 28 - 28 MB | NPU |
| ResNet34-SSD | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 50.488 ms | 18 - 384 MB | NPU |
| ResNet34-SSD | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 121.127 ms | 1 - 288 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 125.64 ms | 16 - 37 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 67.241 ms | 17 - 20 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® QCS8450 | 121.127 ms | 1 - 288 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 114.573 ms | 16 - 36 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 68.228 ms | 28 - 28 MB | NPU |
| ResNet34-SSD | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 42.685 ms | 1 - 250 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 45.559 ms | 4 - 294 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 43.65 ms | 15 - 257 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® X2 Elite | 49.926 ms | 17 - 17 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® X Elite | 76.25 ms | 17 - 17 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 55.524 ms | 16 - 368 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 120.239 ms | 4 - 315 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 128.466 ms | 17 - 36 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 75.265 ms | 17 - 427 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® SA8775P | 117.164 ms | 16 - 251 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® SA8650P | 117.164 ms | 16 - 251 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® SA8255P | 117.164 ms | 16 - 251 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® QCS8450 | 120.239 ms | 4 - 315 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 118.219 ms | 17 - 35 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 76.25 ms | 17 - 17 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 43.65 ms | 15 - 257 MB | NPU |
| ResNet34-SSD | QNN_DLC | float | Qualcomm® SA8295P | 141.167 ms | 0 - 210 MB | NPU |
| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 33.39 ms | 0 - 303 MB | NPU |
| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 40.059 ms | 1 - 265 MB | NPU |
| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 50.656 ms | 9 - 402 MB | NPU |
| ResNet34-SSD | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 116.209 ms | 1 - 330 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 121.697 ms | 0 - 63 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 72.434 ms | 1 - 4 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® SA8775P | 112.908 ms | 1 - 260 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® SA8650P | 112.908 ms | 1 - 260 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® SA8255P | 112.908 ms | 1 - 260 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® QCS8450 | 116.209 ms | 1 - 330 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 113.351 ms | 0 - 63 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 40.059 ms | 1 - 265 MB | NPU |
| ResNet34-SSD | TFLITE | float | Qualcomm® SA8295P | 125.111 ms | 1 - 212 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.
