This model consists of an SMP-UNet trained on the GelGenie training dataset in December 2023. It is highly robust and should produce good results for a wide variety of gel types and resolutions. This is the primary model that should be the first point of reference for any new gel analysis.
The model was trained on the training set (420 images) for 600 epochs and the best checkpoint extracted based on the validation dice score.
For more details on the configuration used for training, please visit
https://huggingface.co/mattaq/GelGenie-Universal-Dec-2023 and check the config.toml file. Our codebase is fully open-sourced and is available here:
https://github.com/mattaq31/GelGenie.
A
BioImage.IO version of this model is also available for download
here.
