README.md

metadata

license: other
library_name: pytorch
pipeline_tag: image-segmentation
tags:
  - remote-sensing
  - change-detection
  - binary-change-detection
  - semantic-segmentation
  - mamba
  - mambarefine-cd
  - mercon
metrics:
  - f1
  - iou
  - precision
  - recall

MambaRefine-CD

MambaRefine-CD is a remote sensing binary change detection model. It takes a bi-temporal image pair as input and outputs a binary change mask. The paper has been accepted at MERCon. This Hugging Face repository contains trained weights, configs, and usage instructions.

Model Details

• Model name: MambaRefine-CD
• Task: binary remote sensing change detection
• Input: pre-change and post-change image pair
• Output: binary change mask
• Framework: PyTorch
• Paper status: Accepted at MERCon
• GitHub repository: https://github.com/Dineth14/MambaRefine-CD

Released Checkpoints

Dataset	Checkpoint	Config	Train Split	Validation Split	Test Split	Main Metric	Notes
WHU-CD	checkpoints/mambarefine_cd_whu_cd_best.pth	configs/whu_cd_run_config.yaml	6096 samples	762 samples	762 samples	Test F1 95.5324	Best validation checkpoint, iteration 45000, threshold 0.55, EMA found.
DSIFN-CD	checkpoints/mambarefine_cd_dsifn_cd_best.pth	configs/dsifn_cd_run_config.yaml	3153 samples	3152 samples	Not specified in selected run manifest	Test F1 96.3963	Best validation checkpoint, iteration 50000, threshold 0.60, EMA found.

Datasets and Splits

WHU-CD

• Official/common dataset name: WHU-CD
• Dataset name used in config: WHU-CD
• Number of image pairs: train 6096, validation 762, test 762
• Image size: 256
• Mask format: binary change mask
• Binary threshold: 127 in configs/active.yaml
• Ignore index: Not specified in selected WHU-CD run config
• Normalization: ImageNet mean [0.485, 0.456, 0.406] and std [0.229, 0.224, 0.225] in src/datasets/transforms.py

DSIFN-CD

• Official/common dataset name: DSIFN-CD
• Dataset name used in config: DSIFN-CD
• Number of image pairs: train 3153, validation 3152, test not specified in selected run manifest
• Image size: 256
• Mask format: binary change mask
• Binary threshold: 127 in configs/active.yaml
• Ignore index: Not specified in selected DSIFN-CD binary release run config
• Normalization: ImageNet mean [0.485, 0.456, 0.406] and std [0.229, 0.224, 0.225] in src/datasets/transforms.py

Results

Dataset	Precision	Recall	F1	IoU	OA	Notes
WHU-CD	96.0072	95.0623	95.5324	91.4469	99.5715	From selected WHU-CD test metrics.
DSIFN-CD	96.2591	96.5340	96.3963	93.0434	97.4721	From selected DSIFN-CD test metrics.

Installation

git clone https://github.com/Dineth14/MambaRefine-CD
cd MambaRefine-CD
pip install -r requirements.txt
pip install huggingface_hub

Download Weights

from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(
    repo_id="<HF_USERNAME_OR_ORG>/MambaRefine0cd",
    filename="checkpoints/mambarefine_cd_whu_cd_best.pth",
)
print(ckpt_path)

Loading the Model

from src.engine.checkpoint import load_checkpoint
from src.models.build import build_model
from src.utils.config import load_config
from src.utils.device import get_device

cfg = load_config("configs/active.yaml")
device = get_device(cfg)
model = build_model(cfg).to(device)
checkpoint = load_checkpoint("checkpoints/mambarefine_cd_whu_cd_best.pth", model)
model.eval()

Evaluation

The repository scripts read configs/active.yaml.

python val.py
python test.py
python infer.py

Before running evaluation with downloaded weights, set checkpoint.path in configs/active.yaml to the downloaded checkpoint path and set the dataset root to your local dataset.

Limitations

• The weights are intended for remote sensing binary change detection.
• Results depend on dataset domain, resolution, preprocessing, and split consistency.
• Users should evaluate with the same preprocessing and splits used during training.
• DSIFN-CD test split count for the selected release run was not specified in the selected run manifest.

Citation

Official citation will be added after the MERCon proceedings information is available.