README.md

RTMDet: Real-time object detection optimized for mobile and edge

RTMDet is a highly efficient model for real-time object detection,capable of predicting both the bounding boxes and classes of objects within an image.It is highly optimized for real-time applications, making it reliable for industrial and commercial use

This is based on the implementation of RTMDet found here. This repository contains scripts for optimized on-device export suitable to run on Qualcomm® devices. More details on model performance accross various devices, can be found here.

Sign up to start using Qualcomm AI Hub and run these models on a hosted Qualcomm® device.

Example & Usage

Install the package via pip:

pip install "qai-hub-models[rtmdet]" torch==2.4.1 -f https://download.openmmlab.com/mmcv/dist/cpu/torch2.4/index.html -f https://qaihub-public-python-wheels.s3.us-west-2.amazonaws.com/index.html

Once installed, run the following simple CLI demo:

python -m qai_hub_models.models.rtmdet.demo

More details on the CLI tool can be found with the --help option. See demo.py for sample usage of the model including pre/post processing scripts. Please refer to our general instructions on using models for more usage instructions.

Export for on-device deployment

This repository contains export scripts that produce a model optimized for on-device deployment. This can be run as follows:

python -m qai_hub_models.models.rtmdet.export

Additional options are documented with the --help option. Note that the above script requires access to Deployment instructions for Qualcomm® AI Hub.

License

• The license for the original implementation of RTMDet can be found here.
• The license for the compiled assets for on-device deployment can be found here

References

• RTMDet: An Empirical Study of Designing Real-Time Object Detectors
• Source Model Implementation

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.