README.md

BIRD

The PyTorch implementation for the BIRD: Bidirectional Temporal Information Propagation for Moving Infrared Dim-small Target Detection.

1. Pre-request

1.1. Environment

conda create -n BIRD python=3.10.11
conda activate BIRD
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch

git clone --depth=1 https://github.com/xiaomingxige/BIRD
cd BIRD
pip install -r requirements.txt

1.2. DCNv2

Build DCNv2

cd nets/ops/dcn/
# You may need to modify the paths of cuda before compiling.
bash build.sh

Check if DCNv2 works (optional)

python simple_check.py

The DCNv2 source files here is different from the open-sourced version due to incompatibility. [issue]

1.3. Datasets

Please follow SSTNet to download the datasets.

2. Train

CUDA_VISIBLE_DEVICES=1 nohup python -u train.py > nohup.out &

Please modify the corresponding file path in train.py before training.

3. Test

We utilize 1 NVIDIA GeForce RTX 3090 GPU for testing：

python vid_map_coco.py

4. Visualization

python vid_predict.py

Acknowledgements

This work is based on SSTNet. Thank UESTC-nnLab for sharing the codes.