This repository is the official implementation of the paper entitled: The Art of Camouflage: Few-shot Learning for Animal Detection and Segmentation Authors: Thanh-Danh Nguyen , Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen, Vinh-Tiep Nguyen, Thanh Duc Ngo, Thanh-Toan Do, Minh-Triet Tran, Tam V. Nguyen*.
Download and install Anaconda with the recommended version from Anaconda Homepage: Anaconda3-2019.03-Linux-x86_64.sh
git clone https://github.com/danhntd/FS-CDIS.git
cd FSCDIS
curl -O https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh
bash Anaconda3-2019.03-Linux-x86_64.sh
After completing the installation, please create and initiate the workspace with the specific versions below. The experiments were conducted on a Linux server with a single GeForce RTX 2080Ti GPU, CUDA 10.1/10.2, Torch 1.7.
conda create --name FSCDIS python=3
conda activate FSCDIS
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.2 -c pytorch
This source code is based on Detectron2. Please refer to INSTALL.md for the pre-built or building Detectron2 from source.
After setting up the dependencies, use the command pip install -e . in this root to finish.
The proposed CAMO-FS is available at this link.
Detectron2 requires a step of data registration for those who want to use the external datasets (Detectron2 Docs).
Our proposed FS-CDIS framework:
The whole script commands can be found in ./scripts/*.
Released checkpoints and results:
We provide the checkpoints of our final model :
| Model R-101 | FS-CDIS-Triplet | FS-CDIS-Memory |
|---|---|---|
| 1-shot | link | link |
| 2-shot | link | link |
| 3-shot | link | link |
| 5-shot | link | link |
Please use the following bibtex to cite this repository:
@article{nguyen2023few,
title={Few-shot Camouflaged Animal Detection and Segmentation},
author={Nguyen, Thanh-Danh and Vu, Anh-Khoa Nguyen and Nguyen, Nhat-Duy and Nguyen, Vinh-Tiep and Ngo, Thanh Duc and Do, Thanh-Toan and Tran, Minh-Triet and Nguyen, Tam V},
journal={arXiv preprint arXiv:2304.07444},
year={2023}
}