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README.md

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+---
+license: mit
+tags:
+  - image-segmentation
+  - satellite-imagery
+  - cloud-detection
+  - cubesat
+  - beavercube
+---
+
+# BeaverCube Cloud Segmentation — SmallCloudNet
+
+U-Net trained to detect clouds in imagery simulated from the Matrix Vision mvBlueFOX-IGC-200w camera
+on the BeaverCube 2 CubeSat (MIT), using the CloudSEN12-L1C Sentinel-2 dataset.
+
+## Model details
+
+| Property | Value |
+|---|---|
+| Architecture | U-Net (SmallCloudNet) |
+| Parameters | 1.86M |
+| Input size (training) | 33×33 px |
+| Input size (inference) | any (fully convolutional) |
+| Classes | clear, thick cloud, thin cloud, shadow |
+
+## Performance
+
+| Metric | Value |
+|---|---|
+| Mean IoU | 0.38 |
+| Mean F1 | 0.54 |
+| Accuracy | 64% |
+| Clear IoU | 0.58 |
+| Thick cloud IoU | 0.45 |
+| Shadow IoU | 0.27 |
+| Thin cloud IoU | 0.22 |
+
+## Usage
+
+```python
+import torch
+from model import SmallCloudNet
+
+model = SmallCloudNet(in_ch=3, num_classes=4)
+checkpoint = torch.load("best_model.pth", map_location="cpu")
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+
+# img: float32 tensor (1, 3, H, W) normalised to [0, 1]
+with torch.no_grad():
+    logits = model(img)               # (1, 4, H, W)
+    mask   = logits.argmax(dim=1)     # (1, H, W)
+```
+
+## Training data
+
+CloudSEN12-L1C (Sentinel-2 L1C), preprocessed to simulate BlueFOX GSD (153.75 m) via:
+1. Gaussian PSF blur (σ = 1.6 px, derived from Kowa 16mm lens Airy disk)
+2. 15.65× INTER_AREA downsample (512×512 → 33×33)
+3. Read + shot noise augmentation applied each epoch