Update README.md

README.md

@@ -9,25 +9,90 @@
 
 ### Installation
 ```bash
-  conda create --name rscd python=3.8
-  conda activate rscd
-  conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia
-  pip install pytorch-lightning==2.0.5
-  pip install scikit-image==0.19.3 numpy==1.24.4
-  pip install torchmetrics==1.0.1
-  pip install -U catalyst==20.09
-  pip install albumentations==1.3.1
-  pip install einops==0.6.1
-  pip install timm==0.6.7
-  pip install addict==2.4.0
-  pip install soundfile==0.12.1
-  pip install ttach==0.0.3
-  pip install prettytable==3.8.0
-  pip install -U openmim
-  pip install triton==2.0.0
-  mim install mmcv
-  pip install -U fvcore
+conda create --name rscd python=3.8
+conda activate rscd
+conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia
+pip install pytorch-lightning==2.0.5
+pip install scikit-image==0.19.3 numpy==1.24.4
+pip install torchmetrics==1.0.1
+pip install -U catalyst==20.09
+pip install albumentations==1.3.1
+pip install einops==0.6.1
+pip install timm==0.6.7
+pip install addict==2.4.0
+pip install soundfile==0.12.1
+pip install ttach==0.0.3
+pip install prettytable==3.8.0
+pip install -U openmim
+pip install triton==2.0.0
+mim install mmcv
+pip install -U fvcore
+```
+### Dataset Preparation
+We evaluate our method on three public datasets: **LEVIR-CD**, **WHU-CD**, and **CLCD**.
 
+| Dataset   | Link |
+|-----------|------|
+| LEVIR-CD  | [Download](https://drive.google.com/file/d/1MEKc9UTM3j4zPFfkvFvjjsynGjZ5tRrF/view?usp=drive_link) |
+| WHU-CD | [Download](https://drive.google.com/file/d/1N73eO20hjtEyYd33M6119U03DYkHjm5i/view?usp=drive_link) |
+| CLCD | [Download](https://drive.google.com/file/d/19eW-Yad3SSiQNuB8WT5XOnvPvjxCp1Cz/view?usp=drive_link) |
+```bash
+Please organize the datasets as follows:
+  rschangedetection
+      ├── rscd (code)
+      ├── work_dirs (save the model weights and training logs)
+      │   └─CLCD_BS4_epoch200 (dataset)
+      │       └─stnet (model)
+      │           └─version_0 (version)
+      │              │  └─ckpts
+      │              │      ├─test (the best ckpts in test set)
+      │              │      └─val (the best ckpts in validation set)
+      │              ├─log (tensorboard logs)
+      │              ├─train_metrics.txt (train & val results per epoch)
+      │              ├─test_metrics_max.txt (the best test results)
+      │              └─test_metrics_rest.txt (other test results)
+      └── data
+          ├── LEVIR_CD
+          │   ├── train
+          │   │   ├── A
+          │   │   │   └── images1.png
+          │   │   ├── B
+          │   │   │   └── images2.png
+          │   │   └── label
+          │   │       └── label.png
+          │   ├── val (the same with train)
+          │   └── test(the same with train)
+          ├── WHU_CD
+          │   ├── train
+          │   │   ├── image1
+          │   │   │   └── images1.png
+          │   │   ├── image2
+          │   │   │   └── images2.png
+          │   │   └── label
+          │   │       └── label.png
+          │   ├── val (the same with train)
+          │   └── test(the same with train)
+          └── CLCD (the same with WHU_CD)
+```
+### Use example
+Training
+```bash
+python train.py -c configs/mamba_cttf.py
+```
+Testing
+```bash
+python test.py \
+-c configs/mamba_cttf.py \
+--ckpt work_dirs/CLCD_BS4_epoch200/mamba_cttf/version_0/ckpts/test/epoch=156.ckpt \
+--output_dir work_dirs/CLCD_BS4_epoch200/mamba_cttf/version_0/ckpts/test \
+```
+Count params and flops
+```bash
+python tools/params_flops.py --size 256
+```
 
-## Dataset Preparation
-  We evaluate our method on three public datasets: **LEVIR-CD**, **WHU-CD**, and **CLCD**.[Download](https://drive.google.com/drive/folders/1zxhJ7v3UPgNsKkdvkYCOW7DdKDAAy_ll) |
+###  Acknowledgement
+Thanks to previous open-sourced repo:
+- [mmsegmentation](https://github.com/open-mmlab/mmsegmentation)
+- [pytorch lightning](https://github.com/Lightning-AI/lightning)
+- [fvcore](https://github.com/facebookresearch/fvcore)