Reindeer detection on satellite imagery

This repository contained code for training and using a model for detecting reindeer on satellite images. The repository is currently a WORK IN PROGRESS.

Installation

In this repository we are using detectron2. To install it follow the procedure:

Create a virtual environment:

python3 -m venv .venv
source .venv/bin/activate

Install detectron2 and the other dependancies:

# FIX for installing detectron2, see: https://github.com/Dao-AILab/flash-attention/issues/253
FLASH_ATTENTION_SKIP_CUDA_BUILD=TRUE pip install flash-attn --no-build-isolation 

# Install dependancies
git clone https://github.com/facebookresearch/detectron2.git
pip install -e detectron2
pip install -r requirements.txt

Activate the virtual environment

source .venv/bin/activate

Import from LabelStudio

We are using [LabelStudio] to annotate the images. After annonating the images in LabelStudio you can export annotations and images as a .zip file and extract it in a folder ./data/annotations.

Then, follow the pipeline!

Create the training dataset dataset

python3 dataset.py

Because the satellite images are too big to be processed efficiently by detectron2 we split the images into tiles. Tile size can be changed in the config.yaml file and can slighlty change the results (we recommand to do a hyperparameter search to search for the optimal tile size).

The script will create a tiles folder containing the tiles of the processed satellite images. The path to the processed satellite images that are to be processed should be updated in the config.yaml file.

Train detectron2

python3 train.py

With this script we train a Detectron2 model with a faster-rcnn architecture as backbone. Note that it is possible to change the backbone by choosing another Detectron compliant model.

The script should create a folder output that contains model_final.pth, the logs and other files that Detectron2 creates.

Predict

python3 predict.py

The script will load the model that has been trained in the previous step to make the predictions. The script will create a folder ./predict/image containing the predicted bounding boxes and ./predict/json containing the a .json file per image documenting all the bounding box coordinates.

In the config.yaml file you can choose to plot the predictions (parameter TRUE / FALSE). If TRUE plots of the satellite pictures with annotations should be created in predict/image/.