---
library_name: pytorch
pipeline_tag: image-to-image
tags:
- cloud-removal
- remote-sensing
- sar
- optical
- sen12ms-cr
- clear-net
- pytorch
---

# CLEAR-Net for SEN12MS-CR

This repository hosts the best CLEAR-Net checkpoint for cloud removal experiments on SEN12MS-CR style SAR and cloudy optical inputs.

The implementation, demo server, and local inference launcher are maintained in the GitHub repository:

**GitHub:** https://github.com/smturtle2/cr-project

## Checkpoint

| File | Description |
| --- | --- |
| `best.pt` | PyTorch checkpoint for `CLEAR_Net(return_decomposition=True)` |

The checkpoint is stored in the project training format. The inference code expects a PyTorch checkpoint containing a `model` state dict.

Checkpoint metadata:

| Field | Value |
| --- | --- |
| Epoch | 87 |
| Global step | 291363 |
| SHA256 | `6b6a001c29d95e9edabd415e5856b83f41bea63c76ef05c5e85312147c697eb4` |

## Test Metrics

Evaluation on the project test split:

| Metric | Value |
| --- | ---: |
| MAE | 0.027414 |
| PSNR | 28.4613 |
| SSIM | 0.893636 |
| SAM | 8.2448 |
| Loss | 0.313022 |

## Usage

Clone the project code:

```bash
git clone https://github.com/smturtle2/cr-project.git
cd cr-project
```

Download the checkpoint:

```bash
hf download Hermanni/clear-net-sen12mscr best.pt --local-dir .
```

Run the local demo:

```bash
./inference.sh ./best.pt
```

If the SEN12MS-CR cache is stored outside the project default path, pass it through the demo arguments:

```bash
./inference.sh ./best.pt --dataset-root /path/to/sen12mscr_cache
```

The demo opens a local CLEAR-Net scene inference server and uses the checkpoint from this repository.

## Intended Use

This checkpoint is intended for research and demonstration of optical cloud removal using SAR-guided restoration on SEN12MS-CR style data.

## Limitations

The model card reports results from the project evaluation pipeline. Performance may differ with different preprocessing, dataset versions, sensor products, or scene tiling settings.