Doodleverse/Segmentation Zoo UNet models for identifying water in orthomosaic photos of coasts.

Doodleverse/Segmentation Zoo UNet models for identifying water in orthomosaic photos of coasts.

Based on Coast Train data

Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I.

These 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

References

* https://github.com/Doodleverse/segmentation_zoo

** https://github.com/Doodleverse/segmentation_gym