Utilizing the same workflow as the tree detection model, we have trained a bird detection model for airborne imagery.
m = main.deepforest() m.use_bird_release()
We have created a GPU colab tutorial to demonstrate the workflow for using the bird model.
For more information, or specific questions about the bird detection, please create issues on the BirdDetector repo
Annotating new images¶
If you would like to train a model, here is a quick video on a simple way to annotate images.
Using a shapefile we could turn it into a dataframe of bounding box annotations by converting the points into boxes
df = shapefile_to_annotations( shapefile="annotations.shp", rgb="image_path", box_points=True, buffer_size=0.15 )
Optionally we can split these annotations into crops if the image is large and will not fit into memory. This is often the case.
df.to_csv("full_annotations.csv",index=False) annotations = preprocess.split_raster( path_to_raster=image_path, annotations_file="full_annotations.csv", patch_size=450, patch_overlap=0, base_dir=directory_to_save_crops, allow_empty=False )
The detector is currently in prep, please check back for a published citation. Cite the github release until a preprint is available.