Awesome
SAM-CD
Pytorch codes of Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images [paper]
The SAM-CD adopts FastSAM as the visual encoder with some modifications.
2024-4-30 Update:
SAM-CD now supports access to efficientSAM. Check the updated model at models/effSAM_CD.py
(prior installation of efficientSAM at the project folder is required). However, direct integration of efficientSAM may cause an accuracy drop, so there is space to further improve the SAM-CD architecture.
How to Use
-
Installation
- Install FastSAM following the instructions.
- Modify the Ultralytics source files following the instructions at: 'SAM-CD/models/FastSAM/README.md'.
-
Dataset preparation.
- Please split the data into training, validation and test sets and organize them as follows:
YOUR_DATA_DIR
├── ...
├── train
│ ├── A
│ ├── B
│ ├── label
├── val
│ ├── A
│ ├── B
│ ├── label
├── test
│ ├── A
│ ├── B
│ ├── label
- Find change line 13 in SAM-CD/datasets/Levir_CD.py (or other data-loading .py files), change
/YOUR_DATA_ROOT/
to your local dataset directory.
-
Training
classic CD training:
python train_CD.py
training CD with the proposed task-agnostic semantic learning:
python train_SAM_CD.py
line 16-45 are the major training args, which can be changed to load different datasets, models and adjust the training settings.
-
Inference and evaluation
inference on test sets: set the chkpt_path and run
python pred_CD.py
evaluation of accuracy: set the prediction dir and GT dir, and run
python eval_CD.py
(More details to be added...)
Dataset Download
In the following, we summarize links to some frequently used CD datasets:
Pretrained Models
For readers to easily evaluate the accuracy, we provide the trained weights of the SAM-CD.
Cite SAM-CD
If you find this work useful or interesting, please consider citing the following BibTeX entry.
@article{ding2024adapting,
title={Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images},
author={Ding, Lei and Zhu, Kun and Peng, Daifeng and Tang, Hao and Yang, Kuiwu and Bruzzone, Lorenzo},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2024},
volume={62},
pages={1-11},
doi={10.1109/TGRS.2024.3368168}
}