Awesome
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
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}
}