Add/improve model card
#1
by
nielsr
HF Staff
- opened
README.md
CHANGED
|
@@ -1,4 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
|
| 2 |
# JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework
|
| 3 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
Code: https://github.com/circleLZY/MTKD-CD.
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
library_name: pytorch
|
| 4 |
+
pipeline_tag: image-to-image
|
| 5 |
+
---
|
| 6 |
|
| 7 |
# JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework
|
| 8 |
|
| 9 |
+
This repository contains the model presented in the paper [JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework](https://arxiv.org/pdf/2502.13407). This model utilizes a multi-teacher knowledge distillation (MTKD) framework for change detection (CD) in remote sensing images.
|
| 10 |
+
|
| 11 |
+
## Dataset
|
| 12 |
+
|
| 13 |
+
The JL1-CD dataset is publicly available and can be downloaded from:
|
| 14 |
+
|
| 15 |
+
- [Google Drive](https://drive.google.com/drive/folders/1ELoqx7J3GrEFMX5_rRynMjW9-Poxz3Uu?usp=sharing)
|
| 16 |
+
- [Baidu Disk](https://pan.baidu.com/s/1_vcO4c5DM5LDuOqLwLrWJg?pwd=5byn)
|
| 17 |
+
- [Hugging Face](https://huggingface.co/datasets/circleLZY/JL1-CD)
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
## Usage
|
| 21 |
+
|
| 22 |
+
### Install
|
| 23 |
+
|
| 24 |
+
To set up the environment, follow the installation instructions provided in the [OpenCD repository](https://github.com/likyoo/open-cd).
|
| 25 |
+
|
| 26 |
+
### Training
|
| 27 |
+
|
| 28 |
+
The training process for the MTKD framework consists of three steps. Below, we use the **Changer-MiT-b0** model as an example:
|
| 29 |
+
|
| 30 |
+
#### Step 1: Train the original model
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
#### Step 2: Train teacher models for different CAR partitions (e.g., 3 partitions)
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
#### Step 3: Train the student model
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
### Testing
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
#### Checkpoints
|
| 47 |
+
|
| 48 |
+
You can download checkpoint files from:
|
| 49 |
+
- [Baidu Disk](https://pan.baidu.com/s/1F5MIGCCiNHFifNl_kDiklA?pwd=4tid)
|
| 50 |
+
- [Hugging Face](https://huggingface.co/circleLZY/MTKD)
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
## Citation
|
| 54 |
+
|
| 55 |
+
If you find the JL1-CD dataset or our work useful in your research, please consider citing our paper:
|
| 56 |
+
|
| 57 |
+
```bibtex
|
| 58 |
+
@article{liu2025jl1,
|
| 59 |
+
title={JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework},
|
| 60 |
+
author={Liu, Ziyuan and Zhu, Ruifei and Gao, Long and Zhou, Yuanxiu and Ma, Jingyu and Gu, Yuantao},
|
| 61 |
+
journal={arXiv preprint arXiv:2502.13407},
|
| 62 |
+
year={2025}
|
| 63 |
+
}
|
| 64 |
+
```
|
| 65 |
+
|
| 66 |
Code: https://github.com/circleLZY/MTKD-CD.
|