UNet is a machine learning model that produces a segmentation mask for an image. The most basic use case will label each pixel in the image as being in the foreground or the background. More advanced usage will assign a class label to each pixel. This version of the model was trained on the data from Kaggle's Carvana Image Masking Challenge (see https://www.kaggle.com/c/carvana-image-masking-challenge) and is used for vehicle segmentation.
This is based on the implementation of Unet-Segmentation 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.unet_segmentation.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.unet_segmentation.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 Unet-Segmentation can be found here.
- The license for the compiled assets for on-device deployment can be found here
- 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.