/Proactive-Object-Detection

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Proactive-Object-Detection

PrObeD:Proactive Object Detection Wrapper

Official Pytorch implementation of Neurips 2023 paper "PrObeD:Proactive Object Detection Wrapper".

Vishal Asnani, Abhinav Kumar, Suya You, Xiaoming Liu

Paper + Supplementary

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Prerequisites

Please look at the environment.yaml file for setting up the environment.

Getting Started

Datasets

  • We use multiple datasets in our paper. For generic object detection (GOD), we use MS-COCO 2017. For camouflaged object detection (COD), we use CAMO, COD10K, and NC4K
  • Please download the datasets from the above links and pepare them according to the requirements of differennt object detector.

Training

  • We incorporate our wrapper on the official implementation of all the detectors. Please refer to the official repositories for all the detectors as mentioned in the papers.
  • We show the training code for Faster R-CNN (GOD detector) and DGNet (COD detector) incorporating our detector.

Faster R-CNN

  • Please download the Faster R-CNN repository from here.
  • Setup the code and data according to the official repository instructions.
  • We change the training code with train_faster_rcnn.py
python train_faster_rcnn.py --dataset coco --net res101 --bs 8 --nw 1 --lr 0.000001 --lr_decay_step 4 --cuda

DGNet

  • Please download the DGNet repository pytorch version from here.
  • Setup the code and data according to the official repository instructions.
  • We change the training code with train_dgnet.py
python train_dgnet.py --gpu_id 0 --model DGNet

Pre-trained model

The pre-trained model for Faster R-CNN and DGNet can be downloaded from below:

Model Link
Faster-RCNN Model
DGNet Coming soon!!

Testing using pre-trained models

  • Download the pre-trained model using the above links.
  • Download the evaluation toolbox for COD here.
  • Provide the model path in the code
  • Run the code as shown below:

Faster R-CNN

python 14_testnet_2.py --dataset coco --net res101 --cuda --model_path "MODEL PATH"

DGNet

Run the below command to generate and save the loca;ization maps.

python test_dgnet_loc.py --gpu_id 0 --model DGNet --model_path "MODEL PATH"

After this, run the evaluation script eval_dgnet.py with the paths of validation data ground-truth and savd predcition to estimate and print all the metrics.

python eval_dgnet.py

Sample pseudo code on how to use our wrapper with any detector

Coming soon!!

If you would like to use our work, please cite:

@inproceedings{asnani2023probed,
  title={PrObeD: Proactive Object Detection Wrapper},
  author={Asnani, Vishal and Kumar, Abhinav and You, Suya and Liu, Xiaoming},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}