Object Detection
PyTorch
android

CenterNet-2D: Optimized for Qualcomm Devices

CenterNet-2D is machine learning model that detects objects by finding their center points.

This is based on the implementation of CenterNet-2D 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
PRECOMPILED_QNN_ONNX float Snapdragon® 8 Elite Gen 5 For Galaxy Mobile QAIRT 2.50, ONNX Runtime 1.27.1 Download
PRECOMPILED_QNN_ONNX float Snapdragon® 8 Elite For Galaxy Mobile QAIRT 2.50, ONNX Runtime 1.27.1 Download
PRECOMPILED_QNN_ONNX float Snapdragon® X2 Elite QAIRT 2.50, ONNX Runtime 1.27.1 Download
PRECOMPILED_QNN_ONNX float Snapdragon® X Elite QAIRT 2.50, ONNX Runtime 1.27.1 Download
PRECOMPILED_QNN_ONNX float Snapdragon® 8 Gen 3 Mobile QAIRT 2.50, ONNX Runtime 1.27.1 Download
PRECOMPILED_QNN_ONNX float Snapdragon® 8 Gen 1 Mobile QAIRT 2.50, ONNX Runtime 1.27.1 Download
PRECOMPILED_QNN_ONNX float Qualcomm® Dragonwing™ IQ-8275 QAIRT 2.50, ONNX Runtime 1.27.1 Download
PRECOMPILED_QNN_ONNX float Qualcomm® Dragonwing™ QCS8550 (Proxy) QAIRT 2.50, ONNX Runtime 1.27.1 Download
PRECOMPILED_QNN_ONNX float Qualcomm® Dragonwing™ IQ-9075 QAIRT 2.50, ONNX Runtime 1.27.1 Download
QNN_CONTEXT_BINARY float Snapdragon® 8 Elite Gen 5 For Galaxy Mobile QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Snapdragon® 8 Elite For Galaxy Mobile QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Snapdragon® X2 Elite QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Snapdragon® X Elite QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Snapdragon® 8 Gen 3 Mobile QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Snapdragon® 8 Gen 1 Mobile QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Qualcomm® Dragonwing™ IQ-8275 QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Qualcomm® Dragonwing™ QCS8550 (Proxy) QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Qualcomm® SA8775P QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Qualcomm® Dragonwing™ IQ-9075 QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Qualcomm® SA7255P QAIRT 2.50 Download
QNN_CONTEXT_BINARY float Qualcomm® SA8295P QAIRT 2.50 Download

For more device-specific assets and performance metrics, visit CenterNet-2D 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 CenterNet-2D on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.object_detection

Model Stats:

  • Input resolution: 1 x 3 x 512 x 512
  • Model checkpoint: ctdet_coco_dla_2x.pth
  • Model size: 37.6 MB
  • Number of parameters: 20.2M

Performance Summary

Model Runtime Precision Chipset Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit
CenterNet-2D PRECOMPILED_QNN_ONNX float Snapdragon® 8 Elite Gen 5 For Galaxy Mobile 160.404 ms 16 - 23 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Snapdragon® 8 Elite For Galaxy Mobile 191.608 ms 14 - 22 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Snapdragon® X2 Elite 139.635 ms 20 - 20 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Snapdragon® X Elite 273.696 ms 52 - 52 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Snapdragon® 8 Gen 3 Mobile 193.941 ms 15 - 23 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Snapdragon® 8 Gen 1 Mobile 277.273 ms 14 - 29 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Qualcomm® Dragonwing™ IQ-8275 241.479 ms 6 - 13 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Qualcomm® Dragonwing™ QCS8550 (Proxy) 286.0 ms 0 - 54 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Qualcomm® QCS8450 277.273 ms 14 - 29 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Qualcomm® Dragonwing™ IQ-9075 268.814 ms 7 - 13 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Qualcomm® Dragonwing™ IQ-X7181 273.696 ms 52 - 52 MB NPU
CenterNet-2D PRECOMPILED_QNN_ONNX float Qualcomm® Dragonwing™ Q-8750 191.608 ms 14 - 22 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Snapdragon® 8 Elite Gen 5 For Galaxy Mobile 160.916 ms 3 - 11 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Snapdragon® 8 Elite For Galaxy Mobile 187.111 ms 3 - 15 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Snapdragon® X2 Elite 140.314 ms 3 - 3 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Snapdragon® X Elite 273.738 ms 3 - 3 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Snapdragon® 8 Gen 3 Mobile 194.047 ms 3 - 11 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Snapdragon® 8 Gen 1 Mobile 278.933 ms 4 - 14 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Qualcomm® Dragonwing™ IQ-8275 241.267 ms 3 - 14 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Qualcomm® Dragonwing™ QCS8550 (Proxy) 278.308 ms 3 - 6 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Qualcomm® SA8650P 279.954 ms 1 - 10 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Qualcomm® SA8255P 279.954 ms 1 - 10 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Qualcomm® QCS8450 278.933 ms 4 - 14 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Qualcomm® Dragonwing™ IQ-9075 268.604 ms 3 - 13 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Qualcomm® Dragonwing™ IQ-X7181 273.738 ms 3 - 3 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Qualcomm® Dragonwing™ Q-8750 187.111 ms 3 - 15 MB NPU
CenterNet-2D QNN_CONTEXT_BINARY float Qualcomm® SA8295P 300.37 ms 0 - 5 MB NPU

License

  • The license for the original implementation of CenterNet-2D can be found here.

References

Community

Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Paper for qualcomm/CenterNet-2D