# ChangeFormer2 **Repository Path**: weichenhik/change-former2 ## Basic Information - **Project Name**: ChangeFormer2 - **Description**: No description available - **Primary Language**: Python - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2023-12-20 - **Last Updated**: 2024-01-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## ChangeFormer: A Transformer-Based Siamese Network for Change Detection > [A Transformer-Based Siamese Network for Change Detection](https://arxiv.org/abs/2201.01293) > [Wele Gedara Chaminda Bandara](https://www.wgcban.com/), and [Vishal M. Patel](https://engineering.jhu.edu/vpatel36/sciencex_teams/vishalpatel/) > Presented at [IGARSS-22](https://www.igarss2022.org/default.php), 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](https://github.com/wgcban/ddpm-cd) - Semi-supervised Change Detection: [SemiCD](https://github.com/wgcban/SemiCD) - Unsupervised Change Detection: [Metric-CD](https://github.com/wgcban/Metric-CD) ## Network Architecture ![image-20210228153142126](./images/IGARS_ChangeFormer.jpeg) ## Quantitative & Qualitative Results on LEVIR-CD and DSIFN-CD ![image-20210228153142126](./images/IGARS_ChangeFormer-LEVIR_DSFIN_both.png) # 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: ```bash conda create --name ChangeFormer --file requirements.txt conda activate ChangeFormer ``` ## Installation Clone this repo: ```shell git clone https://github.com/wgcban/ChangeFormer.git cd ChangeFormer ``` ## Quick Start on LEVIR dataset We have some samples from the [LEVIR-CD](https://justchenhao.github.io/LEVIR/) dataset in the folder `samples_LEVIR` for a quick start. Firstly, you can download our ChangeFormerV6 pretrained model——by [`Github-LEVIR-Pretrained`](https://github.com/wgcban/ChangeFormer/releases/download/v0.1.0/CD_ChangeFormerV6_LEVIR_b16_lr0.0001_adamw_train_test_200_linear_ce_multi_train_True_multi_infer_False_shuffle_AB_False_embed_dim_256.zip). Place it in `checkpoints/ChangeFormer_LEVIR/`. Run a demo to get started as follows: ```python 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`](https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset) dataset in the folder `samples_DSIFN` for a quick start. Download our ChangeFormerV6 pretrained model——by [`Github`](https://github.com/wgcban/ChangeFormer/releases/download/v0.1.0/CD_ChangeFormerV6_DSIFN_b16_lr0.00006_adamw_train_test_200_linear_ce_multi_train_True_multi_infer_False_shuffle_AB_False_embed_dim_256.zip). After downloaded the pretrained model, you can put it in `checkpoints/ChangeFormer_DSIFN/`. Run the demo to get started as follows: ```python 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`](https://github.com/wgcban/ChangeFormer/releases/download/v0.1.0/CD_ChangeFormerV6_LEVIR_b16_lr0.0001_adamw_train_test_200_linear_ce_multi_train_True_multi_infer_False_shuffle_AB_False_embed_dim_256.zip). ``` 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: ```cmd #!/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: ```cmd #!/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`](https://www.dropbox.com/s/18fb5jo0npu5evm/LEVIR-CD256.zip) - DSIFN-CD-256: [`click here to download`](https://www.dropbox.com/s/18fb5jo0npu5evm/LEVIR-CD256.zip) 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: ```cmd wget https://www.dropbox.com/s/18fb5jo0npu5evm/LEVIR-CD256.zip ``` To download DSIFN-CD dataset run following command in linux-terminal: ```cmd 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`](https://justchenhao.github.io/LEVIR/) and [`DSIFN-CD`](https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset). ### 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: ```bibtex @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)