- SIRST
- NUDT-SIRST
- IRSTD-1k
- MWIRSTD
- ACM
- ACLNet
- ISNet
- UIUNet
- DNANet
- SCTransNet
- RDIAN
- EGEUNet
- EffiSegNet
- UNet Series
- ...
Train Code Example
CUDA_VISIBLE_DEVICES=2,3 python train.py --dataset 'SIRST' --model_name 'SCTransNet' --train 1 --test 0 --deep_supervision True --batchsize 16 --epochs 1000 --lr 0.01 --base_size 256 256 --crop_size 256 --optimizer_name 'Adam' --test_epoch 50
Test Code Example
python train.py --dataset 'SIRST' --model_name 'SCTransNet' --train 0 --test 1 --base_size 256 256 --crop_size 256 --save_pred_img True --pth_path your_pth_path
For the SIRST and NUDT-SIRST datasets, it is recommended to employ the parameters --base_size 256 256 --crop_size 256
. Conversely, for the IRSTD-1k dataset, the suggested parameters are --base_size 512 512 --crop_size 512
.
I sincerely appreciate the following outstanding work and code !
Paper List: Awesome Infrared Small Targets