(ECCV 2024) Open-Vocabulary Camouflaged Object Segmentation
@inproceedings{OVCOS_ECCV2024,
title={Open-Vocabulary Camouflaged Object Segmentation},
author={Pang, Youwei and Zhao, Xiaoqi and Zuo, Jiaming and Zhang, Lihe and Lu, Huchuan},
booktitle=ECCV,
year={2024},
}
Prepare Dataset
CAD subset can be found in
- Prepare the training and testing splits: See the document in our dataset for details.
- Set the training and testing splits in the yaml file
env/splitted_ovcamo.yaml:
OVCamo_TR_IMAGE_DIR: Image directory of the training set.OVCamo_TR_MASK_DIR: Mask directory of the training set.OVCamo_TR_DEPTH_DIR: Depth map directory of the training set. Depth maps of the training set which are generated by us, can be downloaded from https://github.com/lartpang/OVCamo/releases/download/dataset-v1.0/depth-train-ovcoser.zipOVCamo_TE_IMAGE_DIR: Image directory of the testing set.OVCamo_TE_MASK_DIR: Mask directory of the testing set.OVCamo_CLASS_JSON_PATH: Path of the json file class_info.json storing class information of the proposed OVCamo.OVCamo_SAMPLE_JSON_PATH: Path of the json file sample_info.json storing sample information of the proposed OVCamo.
Training/Inference
- Install dependencies:
pip install -r requirements.txt.
- The versions of
torch and torchvision are listed in the comment of requirements.txt.
- Run the script to:
- train the model:
python .\main.py --config .\configs\ovcoser.py --model-name OVCoser;
- inference the model:
python .\main.py --config .\configs\ovcoser.py --model-name OVCoser --evaluate --load-from <path of the local .pth file.>.
Evaluate the Pretrained Model
- Download the pretrained model.
- Run the script:
python .\main.py --config .\configs\ovcoser.py --model-name OVCoser --evaluate --load-from model.pth.
Evaluate Our Results
- Download our results and unzip it into
<path>/ovcoser-ovcamo-te.
- Run the script:
python .\evaluate.py --pre <path>/ovcoser-ovcamo-te
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