DDRNet23Slim is a machine learning model that segments an image into semantic classes, specifically designed for road-based scenes. It is designed for the application of self-driving cars.
This is based on the implementation of DDRNet23-Slim 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.
Once installed, run the following simple CLI demo:
python -m qai_hub_models.models.ddrnet23_slim.demoMore 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.
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.ddrnet23_slim.exportAdditional options are documented with the --help option. Note that the above
script requires access to Deployment instructions for Qualcomm® AI Hub.
- The license for the original implementation of DDRNet23-Slim can be found here.
- The license for the compiled assets for on-device deployment can be found here
- Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
- Source Model Implementation
- 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.