Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 RGB images of coasts. Based on SWED*** data https://openmldata.ukho.gov.uk/ These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. RGB (3 band): red, green, blue Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_history.npz' files contain model training metrics One additional file, BEST_MODEL.txt, contains the name of the model with the highest validation accuracy References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym *** https://www.sciencedirect.com/science/article/abs/pii/S0034425722001584