Burdenthrive/cloud-detection-unet-regnetzd8

Cloud Detection — U-Net (RegNetZ D8 encoder)

Repository: Burdenthrive/cloud-detection-unet-regnetzd8
Task: Multiclass image segmentation (4 classes) on multispectral Sentinel‑2 L1C (13 bands) using U‑Net (segmentation_models_pytorch) with RegNetZ D8 encoder.

This model predicts per‑pixel labels among: clear, thick cloud, thin cloud, cloud shadow.

---

✨ Highlights

• Input: 13‑band Sentinel‑2 L1C tiles/patches (float32, shape B×13×512×512).
• Backbone: tu-regnetz_d8 (TIMM encoder via segmentation_models_pytorch).
• Output: Logits B×4×512×512 (apply softmax + argmax).
• Files: model.py, config.json, and weights.

---

📦 Files

• model.py — defines the UNet class (wrapper around smp.Unet).
• config.json — hyperparameters and class names:

  {
    "task": "image-segmentation",
    "model_name": "unet-regnetz-d8",
    "model_kwargs": { "in_channels": 13, "num_classes": 4 },
    "classes": ["clear", "thick cloud", "thin cloud", "cloud shadow"]
  }