* Allow for annotations_file to be none in split_raster, for use in data preprocessing.
1.3.0
* Removed IoU_Callback to better align with pytorch-lightning API, see https://github.com/Lightning-AI/pytorch-lightning/issues/19101 * Refactored evaluate code to use pytorch-lightning evaluation loop directly to calculate results frame during training. * Refactored image_callbacks. Now uses predictions directly, you do not need to specify the root dir or csv file, since it assumed to be evaluation file.
1.1.3
Add box coordinates to evaluate results frame.
1.1.2
If there was more than one class, the precision in the class_recall.csv was computed incorrectly.
1.1.1
Update to project_boxes to include an output for predict_tile and predict_image, the function was split into two. annotations_to_shapefile reverses shapefile_to_annotations. Thanks to sdtaylor for this contribution.
1.1.0
1. Empty frames are now allowed by passing annotations with 0's for all coords. A single row for each blank image.
image_path, 0,0,0,0, "Tree"
2. A check_release function was implemented to reduce github rate limit issues. on use_release(), the local model will be used if check_release = False