Update README.md

README.md

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