This repository contains the code for the evaluation approach proposed in the paper The Overlooked Elephant of Object Detection: Open Set
Our paper may be cited with the following bibtex
@inproceedings{dhamija2020overlooked,
title={The Overlooked Elephant of Object Detection: Open Set},
author={Dhamija, Akshay and Gunther, Manuel and Ventura, Jonathan and Boult, Terrance},
booktitle={The IEEE Winter Conference on Applications of Computer Vision},
pages={1021--1030},
year={2020}
}
Evaluation for the wilderness impact curve is now supported using detectron2
datasets/
in the following structureFor Pascal VOC:
VOC20{07,12}/
JPEGImages/
For MSCOCO:
coco/
{train,val}2017/
# image files that are mentioned in the corresponding json
In order to run the evaluation please prepare a model trained with the protocol files in this repo.
You may use the following command to train a FasterRCNN model:
python main.py --num-gpus 8 --config-file training_configs/faster_rcnn_R_50_FPN.yaml
For convenience models trained with the config files in this repo have been provided at: https://vast.uccs.edu/~adhamija/Papers/Elephant/pretrained_models/
Please ensure your config is correctly set to load the models trained above. You might want to set the OUTPUT_DIR
detectron2 config
The following command may be used to run the complete evaluation
python main.py --num-gpus 2 --config-file training_configs/faster_rcnn_R_50_FPN.yaml --resume --eval-only