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license: mit
pipeline_tag: image-segmentation
library_name: pytorch
tags:
  - unet
  - regnetz_d8
  - segmentation-models-pytorch
  - timm
  - pytorch
  - remote-sensing
  - sentinel-2
  - multispectral
  - cloud-detection
datasets:
  - isp-uv-es/CloudSEN12Plus

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"]
}