Awesome
ChangeFormer: A Transformer-Based Siamese Network for Change Detection
Presented at IGARSS-22, Kuala Lumpur, Malaysia.
Useful links:
- Paper (published): https://ieeexplore.ieee.org/document/9883686
- Paper (ArXiv): https://arxiv.org/abs/2201.01293
- Presentation (in YouTube): https://www.youtube.com/watch?v=SkiNoTrSmQM
My other Change Detection repos:
- Change Detection with Denoising Diffusion Probabilistic Models: DDPM-CD
- Semi-supervised Change Detection: SemiCD
- Unsupervised Change Detection: Metric-CD
Network Architecture
Quantitative & Qualitative Results on LEVIR-CD and DSIFN-CD
Usage
Requirements
Python 3.8.0
pytorch 1.10.1
torchvision 0.11.2
einops 0.3.2
- Please see
requirements.txt
for all the other requirements.
Setting up conda environment:
Create a virtual conda
environment named ChangeFormer
with the following command:
conda create --name ChangeFormer --file requirements.txt
conda activate ChangeFormer
Installation
Clone this repo:
git clone https://github.com/wgcban/ChangeFormer.git
cd ChangeFormer
Quick Start on LEVIR dataset
We have some samples from the LEVIR-CD dataset in the folder samples_LEVIR
for a quick start.
Firstly, you can download our ChangeFormerV6 pretrained model——by Github-LEVIR-Pretrained
.
Place it in checkpoints/ChangeFormer_LEVIR/
.
Run a demo to get started as follows:
python demo_LEVIR.py
You can find the prediction results in samples/predict_LEVIR
.
Quick Start on DSIFN dataset
We have some samples from the DSIFN-CD
dataset in the folder samples_DSIFN
for a quick start.
Download our ChangeFormerV6 pretrained model——by Github
. After downloaded the pretrained model, you can put it in checkpoints/ChangeFormer_DSIFN/
.
Run the demo to get started as follows:
python demo_DSIFN.py
You can find the prediction results in samples/predict_DSIFN
.
Training on LEVIR-CD
When we initialy train our ChangeFormer, we initialized some parameters of the network with a model pre-trained on the RGB segmentation (ADE 160k dataset) to get faster convergence.
You can download the pre-trained model Github-LEVIR-Pretrained
.
wget https://www.dropbox.com/s/undtrlxiz7bkag5/pretrained_changeformer.pt
Then, update the path to the pre-trained model by updating the path
argument in the run_ChangeFormer_LEVIR.sh
.
Here:
https://github.com/wgcban/ChangeFormer/blob/a3eca2b1ec5d0d2628ea2e0b6beae85630ba79d4/scripts/run_ChangeFormer_LEVIR.sh#L28
You can find the training script run_ChangeFormer_LEVIR.sh
in the folder scripts
. You can run the script file by sh scripts/run_ChangeFormer_LEVIR.sh
in the command environment.
The detailed script file run_ChangeFormer_LEVIR.sh
is as follows:
#!/usr/bin/env bash
#GPUs
gpus=0
#Set paths
checkpoint_root=/media/lidan/ssd2/ChangeFormer/checkpoints
vis_root=/media/lidan/ssd2/ChangeFormer/vis
data_name=LEVIR
img_size=256
batch_size=16
lr=0.0001
max_epochs=200
embed_dim=256
net_G=ChangeFormerV6 #ChangeFormerV6 is the finalized verion
lr_policy=linear
optimizer=adamw #Choices: sgd (set lr to 0.01), adam, adamw
loss=ce #Choices: ce, fl (Focal Loss), miou
multi_scale_train=True
multi_scale_infer=False
shuffle_AB=False
#Initializing from pretrained weights
pretrain=/media/lidan/ssd2/ChangeFormer/pretrained_segformer/segformer.b2.512x512.ade.160k.pth
#Train and Validation splits
split=train #train
split_val=test #test, val
project_name=CD_${net_G}_${data_name}_b${batch_size}_lr${lr}_${optimizer}_${split}_${split_val}_${max_epochs}_${lr_policy}_${loss}_multi_train_${multi_scale_train}_multi_infer_${multi_scale_infer}_shuffle_AB_${shuffle_AB}_embed_dim_${embed_dim}
CUDA_VISIBLE_DEVICES=1 python main_cd.py --img_size ${img_size} --loss ${loss} --checkpoint_root ${checkpoint_root} --vis_root ${vis_root} --lr_policy ${lr_policy} --optimizer ${optimizer} --pretrain ${pretrain} --split ${split} --split_val ${split_val} --net_G ${net_G} --multi_scale_train ${multi_scale_train} --multi_scale_infer ${multi_scale_infer} --gpu_ids ${gpus} --max_epochs ${max_epochs} --project_name ${project_name} --batch_size ${batch_size} --shuffle_AB ${shuffle_AB} --data_name ${data_name} --lr ${lr} --embed_dim ${embed_dim}
Training on DSIFN-CD
Follow the similar procedure mentioned for LEVIR-CD. Use run_ChangeFormer_DSIFN.sh
in scripts
folder to train on DSIFN-CD.
Evaluate on LEVIR
You can find the evaluation script eval_ChangeFormer_LEVIR.sh
in the folder scripts
. You can run the script file by sh scripts/eval_ChangeFormer_LEVIR.sh
in the command environment.
The detailed script file eval_ChangeFormer_LEVIR.sh
is as follows:
#!/usr/bin/env bash
gpus=0
data_name=LEVIR
net_G=ChangeFormerV6 #This is the best version
split=test
vis_root=/media/lidan/ssd2/ChangeFormer/vis
project_name=CD_ChangeFormerV6_LEVIR_b16_lr0.0001_adamw_train_test_200_linear_ce_multi_train_True_multi_infer_False_shuffle_AB_False_embed_dim_256
checkpoints_root=/media/lidan/ssd2/ChangeFormer/checkpoints
checkpoint_name=best_ckpt.pt
img_size=256
embed_dim=256 #Make sure to change the embedding dim (best and default = 256)
CUDA_VISIBLE_DEVICES=0 python eval_cd.py --split ${split} --net_G ${net_G} --embed_dim ${embed_dim} --img_size ${img_size} --vis_root ${vis_root} --checkpoints_root ${checkpoints_root} --checkpoint_name ${checkpoint_name} --gpu_ids ${gpus} --project_name ${project_name} --data_name ${data_name}
Evaluate on DSIFN
Follow the same evaluation procedure mentioned for LEVIR-CD. You can find the evaluation script eval_ChangeFormer_DSFIN.sh
in the folder scripts
. You can run the script file by sh scripts/eval_ChangeFormer_DSIFN.sh
in the command environment.
Dataset Preparation
Data structure
Change detection data set with pixel-level binary labels;
├─A
├─B
├─label
└─list
A
: images of t1 phase;
B
:images of t2 phase;
label
: label maps;
list
: contains train.txt, val.txt and test.txt
, each file records the image names (XXX.png) in the change detection dataset.
Links to processed datsets used for train/val/test
You can download the processed LEVIR-CD
and DSIFN-CD
datasets by the DropBox through the following here:
- LEVIR-CD-256:
click here to download
- DSIFN-CD-256:
click here to download
Since the file sizes are large, I recommed to use command line and cosider downloading the zip file as follows (in linux):
To download LEVIR-CD dataset run following command in linux-terminal:
wget https://www.dropbox.com/s/18fb5jo0npu5evm/LEVIR-CD256.zip
To download DSIFN-CD dataset run following command in linux-terminal:
wget https://www.dropbox.com/s/18fb5jo0npu5evm/LEVIR-CD256.zip
For your reference, I have also attached the inks to original LEVIR-CD and DSIFN-CD here: LEVIR-CD
and DSIFN-CD
.
Other useful notes
ChangeFormer for multi-class change detection
If you wish to use ChangeFormer for multi-class change detection, you will need to make a few modifications to the existing codebase, which is designed for binary change detection. There are many discussions in the issues section. The required modifications are (https://github.com/wgcban/ChangeFormer/issues/93#issuecomment-1918609871):
run_ChangeFormer_cd.sh
: n_class=8 and make it a hyperparameter to python main.pymodels/networks.py
: net = ChangeFormerV6(embed_dim=args.embed_dim, output_nc=args.n_class)models/basic_model.py
: Comment out: pred_vis = pred * 255, i.e., modifications to visualisation processingmodels/trainer.py
: Modify: ConfuseMatrixMeter(n_class=self.n_class)
License
Code is released for non-commercial and research purposes only. For commercial purposes, please contact the authors.
Citation
If you use this code for your research, please cite our paper:
@INPROCEEDINGS{9883686,
author={Bandara, Wele Gedara Chaminda and Patel, Vishal M.},
booktitle={IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
title={A Transformer-Based Siamese Network for Change Detection},
year={2022},
volume={},
number={},
pages={207-210},
doi={10.1109/IGARSS46834.2022.9883686}}
Disclaimer
Appreciate the work from the following repositories:
- https://github.com/justchenhao/BIT_CD (Our ChangeFormer is implemented on the code provided in this repository)