See the following pages for more details:
- Project page: IncidentsDataset.csail.mit.edu.
- ECCV 2020 Paper "Detecting natural disasters, damage, and incidents in the wild" here.
- Extended Paper "Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents" here.
Please fill out this form and then email/notify [email protected] to request the data.
The data structure is in JSON with URLs and labels. The files are in the following form:
# single-label multi-class (ECCV 2020 version):
eccv_train.json
eccv_val.json
# multi-label multi-class (latest version):
multi_label_train.json
multi_label_val.json
-
Download chosen JSON files and move to the data folder.
-
Look at VisualizeDataset.ipynb to see the composition of the dataset files.
-
Download the images at the URLs specified in the JSON files.
-
Take note of image download location. This is param
--images_path
in parser.py.
git clone https://github.com/ethanweber/IncidentsDataset
cd IncidentsDataset
conda create -n incidents python=3.8.2
conda activate incidents
pip install -r requirements.txt
-
Download pretrained weights here. Place desired files in the pretrained_weights folder. Note that these take the following structure:
# run this script to download everything python run_download_weights.py # pretrained weights with Places 365 resnet18_places365.pth.tar resnet50_places365.pth.tar # ECCV baseline model weights eccv_baseline_model_trunk.pth.tar eccv_baseline_model_incident.pth.tar eccv_baseline_model_place.pth.tar # ECCV final model weights eccv_final_model_trunk.pth.tar eccv_final_model_incident.pth.tar eccv_final_model_place.pth.tar # multi-label final model weights multi_label_final_model_trunk.pth.tar multi_label_final_model_incident.pth.tar multi_label_final_model_place.pth.tar
-
Run inference with the model with RunModel.ipynb.
-
Compute mAP and report numbers.
# test the model on the validation set python run_model.py \ --config=configs/eccv_final_model \ --mode=val \ --checkpoint_path=pretrained_weights \ --images_path=/path/to/downloaded/images/folder/
-
Train a model.
# train the model python run_model.py \ --config=configs/eccv_final_model \ --mode=train \ --checkpoint_path=runs/eccv_final_model # visualize tensorboard tensorboard --samples_per_plugin scalars=100,images=10 --port 8880 --bind_all --logdir runs/eccv_final_model
See the
configs/
folder for more details.
If you find this work helpful for your research, please consider citing our paper:
@InProceedings{weber2020eccv,
title={Detecting natural disasters, damage, and incidents in the wild},
author={Weber, Ethan and Marzo, Nuria and Papadopoulos, Dim P. and Biswas, Aritro and Lapedriza, Agata and Ofli, Ferda and Imran, Muhammad and Torralba, Antonio},
booktitle={The European Conference on Computer Vision (ECCV)},
month = {August},
year={2020}
}
This work is licensed with the MIT License. See LICENSE for details.
This work is supported by the CSAIL-QCRI collaboration project and RTI2018-095232-B-C22 grant from the Spanish Ministry of Science, Innovation and Universities.