bobleono / OCENet

Online Confidence Estimation - OCENet

The implementation of Modeling Aleatoric Uncertainty for Camouflaged Object Detection, Jiawei Liu, Jing Zhang and Nick Barnes, [Paper].

Citation

If you find the code useful, please consider citing our paper using the following BibTeX entry.

@InProceedings{Liu_2022_WACV,
    author    = {Liu, Jiawei and Zhang, Jing and Barnes, Nick},
    title     = {Modeling Aleatoric Uncertainty for Camouflaged Object Detection},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2022},
    pages     = {1445-1454}
}

Requirements

• Python 3.6
• pytorch 1.3.0, torchvision 0.4.1
• CUDA 10.1
• 2 x 2080Ti GPU

Download COD10K training set through Google Drive link or Baidu Pan link with the fetch code:djq2.

Usage

1. Training

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_port=3000 train.py

2. Inference

Download the weight of CODNet and OCENet from the [Google Drive]

CUDA_VISIBLE_DEVICES=0 python test.py

3. Evaluation

Please use the SOD evaluation toolbox provided by jiwei0921.

Results

The prediction results can be downloaded from [Google Drive]