Awesome
<div align=center> <a src="https://img.shields.io/badge/%F0%9F%93%96-Arxiv_2410.17598-red.svg?style=flat-square" href="https://arxiv.org/abs/2410.17598"> <img src="https://img.shields.io/badge/%F0%9F%93%96-Arxiv_2410.17598-red.svg?style=flat-square"> </a> <a src="https://img.shields.io/badge/%F0%9F%9A%80-SUSTech_VIP_Lab-ed6c00.svg?style=flat-square" href="https://zhengfenglab.com/"> <img src="https://img.shields.io/badge/%F0%9F%9A%80-SUSTech_VIP_Lab-ed6c00.svg?style=flat-square"> </a> </div>PlantCamo Dataset is the first dataset dedicated for plant camouflage detection. It contains over 1,000 images with plant camouflage characteristics.
Demo
https://github.com/yjybuaa/PlantCamo/assets/39208339/766645f1-6951-4cf0-baf1-8a5fb4b13bd9
Download the Dataset and Results
PlantCamo-full(Code: wnyc) Google drive link
PlantCamo-Train-and-Test(Code: hq87) Google drive link
Results(Code: 6o76) Google drive link
Usage
The training and testing experiments are conducted using PyTorch with a single RTX 3090 GPU of 24 GB Memory.
Download
pvt_v2_b2.pth
at here (Code: gy87) or Google drive link, and put it into.\pretrained_pvt
Train
Download PlantCamo-Train-and-Test
at here(Code: hq87) or Google drive link, and put it into .\datasets
Test
Download trained model Net_epoch_best.pth
at here(Code: b98f) or Google drive link, and put it into .\ckpt
Evaluation
You can find it in https://github.com/lartpang/PySODMetrics or you can run the metric_caller.py
Citation
We appreciate your support of our work!
@article{plantcamo,
title={PlantCamo: Plant Camouflage Detection},
author={Jinyu Yang and Qingwei Wang and Feng Zheng and Peng Chen and Aleš Leonardis and Deng-Ping Fan},
journal={CAAI Artificial Intelligence Research (AIR)},
year={2025}
}