Repo for using Keras Retinanet for seabird detection in drone imagery
-
Install Docker on Ubuntu (https://docs.docker.com/engine/install/ubuntu/)
-
Install Git
-
Clone this repo
git clone https://github.com/madelinehayes/seabirdNET.git
- Move into that directory
cd seabirdNET
- Create a Docker Image and Container based on the Dockerfile in this repo
docker build -t <img_name> Dockerfile
docker run --name <cont_name> -it -p 8888:8888 -p 6006:6006 -v ~/:/host <img_name>
- Create a Docker Image and Container for your deep learning environment based on the GPU Dockerfile in this repo
docker build -t <img_name> DockerfileGPU
docker run --name <cont_name> -it -p 8888:8889 -p 6006:6006 -v ~/:/host <img_name>
Your Docker container is now running. Exit that container
exit
To restart your container and attach it to the terminal
docker start <cont_name>
docker attach <cont_name>
Now start jupyter:
jupyter notebook --allow-root --ip 0.0.0.0 /host
- Now install Keras Retinanet following the instructions here: https://github.com/fizyr/keras-retinanet
uas_img_handler_FINAL.ipynb
is used to split the orthomosaics created from drone imagery into smaller tiles- The tiles created from
uas_img_handler_FINAL.ipynb
can be imported into VIA http://www.robots.ox.ac.uk/~vgg/software/via/app/via_image_annotator.html via_to_retinanet_FINAL
is used to convert the output of VIA into the format Retinanet requires for training dataalbatross_detections_FINAL.ipynb
andpenguin_detections_FINAL.ipynb
trains the model, runs validation and testing, and runs inference on the tiles created fromuas_img_handler_FINAL.ipynb
export_detections_FINAL.ipynb
ingests the output from the Retinanet detections and converts into geolocated shapefiles