This project seeks to make conservation organizations more efficient by automating the process of labeling images taken by motion-activated "camera traps" according to the kinds of animals that appear in them. See this article for more information.
If necessary, create an AWS account, install the AWS CLI tool (pip install awscli), and set up your AWS config and credentials (aws configure).
All of the commands below are written to run from the repo root.
Download a preprocessed version of our primary dataset to autofocus/data/lpz_2016_2017/processed (you can change the destination directory if you like):
FILENAME=lpz_2016_2017_processed.tar.gz
aws s3 cp s3://autofocus/lpz_data/${FILENAME} $(pwd)/data/lpz_2016_2017/Unpack the tarfile:
tar -xvf $(pwd)/data/lpz_2016_2017/${FILENAME}Delete the tarfile:
rm $(pwd)/data/lpz_2016_2017/${FILENAME}This dataset contains approximately 80,000 images and a CSV of labels and image metadata. It occupies 17.1GB uncompressed, so you will need about 40GB free for the downloading and untarring process. The images have been preprocessed by trimming the bottom 198 pixels (which often contains a metadata footer that could only mislead a machine learning model) and resizing to be 512 pixels along their shorter dimension. In addition, the labels have been cleaned up and organized.
If you would like to work with data that has not been preprocessed as described above, replace FILENAME=lpz_2016_2017_processed.tar.gz with FILENAME=data_2016_2017.tar.gz. You will need to have about 100GB free to download and untar the raw data.
autofocus/build_dataset/lpz_2016_2017/process_raw.py contains the code that was used to generate the processed data from the raw data.
Download a trained multilabel fast.ai model:
aws s3 cp s3://autofocus/models/multilabel_model_20190407.pkl $(pwd)/autofocus/predict/modelsautofocus/train_model/train_multilabel_model.ipynb contains the code that was used to train and evaluate this model.
autofocus/predict contains code for a Flask app that serves predictions from a trained fast.ai model. See the README in that directory for more information.