/Weakly-Supervised-Camouflaged-Object-Detection-with-Scribble-Annotations

Code for the AAAI 2023 paper "Weakly-Supervised Camouflaged Object Detection with Scribble Annotations"

Primary LanguagePythonOtherNOASSERTION

Weakly-Supervised Camouflaged Object Detection with Scribble Annotations (AAAI23, ORAL)

Authors: Ruozhen He*, Qihua Dong*, Jiaying Lin, and Rynson Lau (* joint first authors)

Paper Link: arxiv

CRNet

Dataset

  • We relabeled 4,040 images (3,040 from COD10K, 1,000 from CAMO) with scribbles and proposed the S-COD dataset (Download) for training. In our annotations, "1" stands for foregrounds, "2" for backgrounds, and "0" for unlabeled regions.
  • Download the training dataset (COD10K-train) at here.
  • Download the testing dataset (COD10K-test + CAMO-test + CHAMELEON) at here.

Experimental Results

Evaluation

Code

Requirements

git clone --recurse-submodules https://github.com/dddraxxx/Weakly-Supervised-Camouflaged-Object-Detection-with-Scribble-Annotations.git
pip install -r requirements.txt

Pretrained weights

The pretrained weight can be found here: ResNet-50.

Train

  • Download the dataset and pretrained model. (examples of train.txt and test.txt are in the path ./CodDataset)
  • Modify the path in train.py.
  • Run python train.py.

Test and Evaluate

  • The evaluation is done using the submodule PySODEvalToolKit. Add the json files according to its instruction.
  • Modify the path and filename.
  • Run python test.py.

Credit

The code is partly based on SCWSSOD, GCPANet and GatedCRFLoss.