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--- |
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license: mit |
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pipeline_tag: image-segmentation |
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library_name: pytorch |
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tags: |
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- segformer |
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- mit-b4 |
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- transformer |
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- segmentation-models-pytorch |
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- timm |
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- pytorch |
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- remote-sensing |
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- sentinel-2 |
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- rgb |
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- cloud-detection |
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datasets: |
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- "isp-uv-es/CloudSEN12Plus" |
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--- |
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# Cloud Detection — SegFormer (MiT-B4 encoder, RGB) |
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**Repository:** `Burdenthrive/cloud-detection-segformer-mit-b4` |
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**Task:** Multiclass semantic segmentation (4 classes) on **Sentinel‑2 L1C RGB** (3 bands) using **SegFormer** (`segmentation_models_pytorch`) with **MiT‑B4** encoder. |
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This model predicts per‑pixel labels among: **clear**, **thick cloud**, **thin cloud**, **cloud shadow**. |
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--- |
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## ✨ Highlights |
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- **Input:** Sentinel‑2 L1C RGB tiles/patches (float32, shape `B×3×512×512`, bands **B04‑B03‑B02**). |
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- **Backbone:** `mit_b4` (MiT encoder via `segmentation_models_pytorch`). |
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- **Output:** Logits `B×4×512×512` (apply softmax + argmax). |
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- **Files:** `model.py`, `config.json`, and weights. |
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--- |
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## 📦 Files |
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- `model.py` — defines the `SegFormer` class (wrapper around `smp.Segformer`). |
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- `config.json` — hyperparameters and class names: |
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```json |
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{ |
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"task": "image-segmentation", |
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"model_name": "segformer-mit-b4", |
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"model_kwargs": { |
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"encoder_name": "mit_b4", |
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"encoder_weights": "imagenet", |
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"in_channels": 3, |
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"num_classes": 4, |
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"freeze_encoder": false |
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}, |
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"classes": ["clear", "thick cloud", "thin cloud", "cloud shadow"], |
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"id2label": { "0": "clear", "1": "thick cloud", "2": "thin cloud", "3": "cloud shadow" }, |
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"label2id": { "clear": 0, "thick cloud": 1, "thin cloud": 2, "cloud shadow": 3 }, |
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"input_bands": ["B04", "B03", "B02"] |
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} |
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