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Pytorch implementation for "Iterative Human and Automated Identification of Wildlife Images" (Nature -Machine Intelligence, 2021)

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Iterative Human and Automated Identification of Wildlife Images

[Paper] [Preprint] [Press]

*This repository is the author's re-implementation of the iterative recognition system with machine and humans in the loop described in:

"Iterative Human and Automated Identification of Wildlife Images" Zhongqi Miao*, Ziwei Liu*, Kaitlyn M. Gaynor, Meredith S. Palmer, Stella X. Yu, Wayne M, Getz in Nature - Machine Intelligence, 2021*

Further information please contact Zhongqi Miao and Ziwei Liu.

Requirements

Data

All raw camera trap images that were used in this study (except classes with humans), along with the associated annotation information, are uploaded to the publicly-available Labeled Information Library of Alexandria: Biology and Conservation (LILA BC), and can be downloaded [here].

Changing dataset root for training and testing

Once the data is downloaded, please change the data root in the configuration files. For example: dataset_root: /Mozambique.

Stage 1: pre-training and evaluation

python main.py --config ./configs/Stage_1/plain_resnet_MOZ_S1_101920.yaml

Stage 1: energy fine-tuning

python main.py --config ./configs/Stage_1/energy_resnet_MOZ_S1_101920.yaml --energy_ft

Stage 2: training

python main.py --config ./configs/Stage_2/pslabel_oltr_resnet_MOZ_S2_111120.yaml

Stage 2: energy fine-tuning

python main.py --config ./configs/Stage_2/pslabel_oltr_energy_resnet_MOZ_S2_111620.yaml

Stage 2: deploying

python main.py --config ./configs/Stage_2/pslabel_oltr_energy_resnet_MOZ_S2_111620.yaml --deploy

Demo

A demo of this code can be found in [here] in CodeOcean.

License and Citation

The use of this software is released under BSD-3.

@article{10.1038/s42256-021-00393-0, 
year = {2021}, 
title = {{Iterative human and automated identification of wildlife images}}, 
author = {Miao, Zhongqi and Liu, Ziwei and Gaynor, Kaitlyn M and Palmer, Meredith S and Yu, Stella X and Getz, Wayne M}, 
journal = {Nature Machine Intelligence}, 
doi = {10.1038/s42256-021-00393-0}, 
pages = {885--895}, 
number = {10}, 
volume = {3}
}

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