Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/likyoo/BAN
The pytorch implementation for "A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection"
https://github.com/likyoo/BAN
change-detection computer-vision deep-learning remote-sensing-image
Last synced: about 2 months ago
JSON representation
The pytorch implementation for "A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection"
- Host: GitHub
- URL: https://github.com/likyoo/BAN
- Owner: likyoo
- License: apache-2.0
- Created: 2023-11-28T01:01:11.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-10T01:06:13.000Z (9 months ago)
- Last Synced: 2024-08-17T10:18:08.410Z (5 months ago)
- Topics: change-detection, computer-vision, deep-learning, remote-sensing-image
- Language: Python
- Homepage: https://arxiv.org/abs/2312.01163
- Size: 706 KB
- Stars: 69
- Watchers: 3
- Forks: 2
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-remote-sensing-change-detection - Li K, Cao X, Meng D. A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection
README
# BAN
Official implementation for the paper ["A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection"](https://arxiv.org/abs/2312.01163), the code is developed on top of [Open-CD v1.1.0](https://github.com/likyoo/open-cd/tree/f05e7fdfe1e0f9a9afa9d6c063ea03b43345226e).
## News
- 2/10/2024 - BAN is supported in [Open-CD](https://github.com/likyoo/open-cd). :yum:
## Usage
### Install
- Create a conda virtual environment and activate it:
```bash
conda create -n BAN python=3.8 -y
conda activate BAN
```
- Install `PyTorch` and `torchvision` :
For examples, to install torch==2.0.0 with CUDA==11.8:
```bash
pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118
```
- Install `OpenMMLab` Toolkits as Python packages:
```bash
pip install -U openmim
mim install mmengine==0.10.1
mim install mmcv==2.1.0
mim install mmpretrain==1.1.1
pip install mmsegmentation==1.2.2
pip install mmdet==3.2.0
```
- Install Open-CD
```bash
git clone https://github.com/likyoo/open-cd.git
cd open-cd
pip install -v -e .
cd ..
```
- Install other requirements:
```bash
pip install ftfy regex
```
- Clone this repo:
```bash
git clone https://github.com/likyoo/BAN.git
cd BAN
```
### Data Preparation
Download datasets ([LEVIR-CD](https://justchenhao.github.io/LEVIR/), [S2Looking](https://github.com/S2Looking/Dataset), [BANDON](https://github.com/fitzpchao/BANDON), [WHU-CD](https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html)) and move (or link) them to `BAN/data`.
### Evaluation
To evaluate our `BAN` on LEVIR-CD test, run:
```bash
python test.py
```
You can download checkpoint files from [huggingface](https://huggingface.co/likyoo/BAN/tree/main/checkpoint) | [baidu disk](https://pan.baidu.com/s/1RkIGsOB3XBi7Oi6mKIpZ2w?pwd=kfp9).
For example, to evaluate the `BAN-vit-l14-clip_mit-b0` with a single GPU:
```bash
python test.py configs/ban/ban_vit-l14-clip_mit-b0_512x512_40k_levircd.py checkpoint/ban_vit-l14-clip_mit-b0_512x512_40k_levircd.pth
```
### Training
To train the `BAN`, run:
```bash
python train.py
```
For example, to train the `BAN-vit-l14-clip_mit-b0` with a single GPU on LEVIR-CD, run:
```bash
python train.py configs/ban/ban_vit-l14-clip_mit-b0_512x512_40k_levircd.py
```
**Note**: You can download pretrained files from [huggingface](https://huggingface.co/likyoo/BAN/tree/main/pretrain) | [baidu disk](https://pan.baidu.com/s/1RkIGsOB3XBi7Oi6mKIpZ2w?pwd=kfp9).
### Citation
```bibtex
@ARTICLE{10438490,
author={Li, Kaiyu and Cao, Xiangyong and Meng, Deyu},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection},
year={2024},
volume={},
number={},
pages={1-1},
keywords={Adaptation models;Task analysis;Data models;Computational modeling;Feature extraction;Transformers;Tuning;Change detection;foundation model;visual tuning;remote sensing image processing;deep learning},
doi={10.1109/TGRS.2024.3365825}}
```