ML_object_detection

Maintained by the Danish Climate Data Agency for Bounding Box Detection on Oblique Images.

Installation

• clone repo

  git clone https://github.com/SDFIdk/ML_object_detection

• create conda environment

  cd ML_object_detection
  mamba env create -f environment.yml
  mamba activate  ML_object_detection

Example use

• Use the included model for inference on one small and one large image from the included set of example images

  python src/ML_object_detection/infer_with_sahi.py --weights models/example_model.pt --folder_with_images data/example_images/ --result_folder output

Create dataset for training

• split the images to sizes suitable for yolo

  python split_with_gdal.py --image /path/to/large/images --output dataset/folder --x 640 --y 640 --overlap 40

• Create a dataset with labelme

  Open folder containing the splitted images

  For each image you want to train on, draw rectangles from the upper left corner to the lower right corner.

  OBS. All objects of the categoriez you want to detect needs to be marked up. Partly labeled images will ruin the training.

• (optional) set all "unkown"/"ignore" areas to black

  If you labeled areas with the text "ignore" (e.g areas for wich you are unsure about the correct classification) we have the option to mask all these areas and make them black. Calling mask_unknown_regions.py with -h flag will give more instructions on usage

  python  mask_unknown_regions.py -h

• make sure that all .json files use the same format (original .tif image)

python standardize_json.py --json_dir /mnt/T/mnt/trainingdata/object_detection/from_Fdrev_ampol/all/

• convert the labelme dataset to yolo format with

  labelme2yolo --json_dir /path/to/labelme_json_dir/ --val_size 0.15 --test_size 0.15
  e.g labelme2yolo --json_dir /mnt/T/mnt/trainingdata/object_detection/from_Fdrev_ampol/split/

• Train a object detection model on the dataset

  python train.py --data /path/to/labelme_json_dir/config.yml
  e.g  python src/ML_object_detection/train.py --data /mnt/T/mnt/trainingdata/object_detection/object_detection_dataset/2025-06-16/labelme_images/YOLODataset/dataset.yaml

• Use the model for inference on large (unsplitted images) (e.g for creating sugestions for new labels)

  python src/ML_object_detection/infer_with_sahi.py --weights models/example_model.pt --folder_with_images data/example_images/ --result_folder output