Create README.md

README.md

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+---
+tags:
+- computer-vision
+- semantic-segmentation
+- satellite-imagery
+- unet
+- remote-sensing
+datasets:
+- CloudCover_SatelliteImagery_256x256
+license: cc-by-nc-4.0
+model-index:
+- name: SatelliteImage_CloudSegmentation_Unet
+  results:
+  - task:
+      name: Semantic Segmentation
+      type: image-segmentation
+    metrics:
+    - type: iou
+      value: 0.915
+      name: Mean Intersection over Union (IoU)
+    - type: dice_score
+      value: 0.950
+      name: Cloud Dice Score
+---
+
+# SatelliteImage_CloudSegmentation_Unet
+
+## 🛰️ Overview
+
+The **SatelliteImage_CloudSegmentation_Unet** is a **U-Net** based model designed for **semantic segmentation** of satellite imagery. Its purpose is to accurately classify every pixel in an input image as either "Cloud" or "Background/Clear Sky." This is critical for pre-processing Earth Observation (EO) data before tasks like land cover mapping or atmospheric correction. 
+
+## 🧠 Model Architecture
+
+The model employs the classic U-Net architecture, which is highly effective for biomedical and remote sensing segmentation due to its symmetric encoder-decoder structure with skip connections.
+
+* **Encoder (Contracting Path):** Consists of repeated convolutional and pooling layers to capture contextual information and build high-level feature maps.
+* **Decoder (Expanding Path):** Uses up-convolutional layers to increase the resolution of the feature maps.
+* **Skip Connections:** Directly connect feature maps from the encoder to the corresponding layers in the decoder. This is vital for preserving fine-grained details needed for precise boundary localization.
+* **Input:** RGB satellite image patches of size 256x256.
+* **Output:** A 256x256 pixel-wise mask with 2 channels, representing the probability distribution for the two classes (Cloud and Background).
+
+## 🎯 Intended Use
+
+This model is intended for use in remote sensing and geospatial applications:
+
+1.  **EO Data Pre-processing:** Automatically generating masks to filter out cloudy regions, ensuring the reliability of subsequent land-use classification or agricultural monitoring.
+2.  **Atmospheric Science:** Quantifying cloud fraction and distribution over large geographic areas for climate modeling.
+3.  **Disaster Response:** Quickly assessing the visibility of ground features (e.g., flood extent) after a weather event.
+
+## ⚠️ Limitations
+
+1.  **Thin/Cirrus Clouds:** The model may struggle with very thin, semi-transparent cirrus clouds, often misclassifying them as clear sky due to low contrast.
+2.  **Shadows:** Cloud shadows on the ground can sometimes be mistakenly classified as cloud due to their low brightness values.
+3.  **Resolution Dependence:** Trained on 256x256 patches. Applying the model directly to very high-resolution images (e.g., 4k) without appropriate tiling and handling may lead to boundary artifacts.
+
+---
+
+### MODEL 2: **Toxicology_StructureToxicity_GNN**
+
+This model is a Graph Neural Network (GNN) for predicting chemical toxicity based on molecular graph structure.
+
+#### config.json
+
+```json
+{
+  "_name_or_path": "custom-graph-tox-predictor",
+  "architectures": [
+    "GraphConvolutionalNetwork"
+  ],
+  "model_type": "molecular_property_prediction",
+  "graph_type": "molecular_graph",
+  "node_features": 74,
+  "edge_features": 12,
+  "num_gcn_layers": 3,
+  "hidden_dim": 128,
+  "global_pooling": "readout_mean",
+  "output_dim": 1,
+  "task_type": "binary_classification",
+  "id2label": {
+    "0": "Non-Toxic",
+    "1": "Toxic"
+  },
+  "label2id": {
+    "Non-Toxic": 0,
+    "Toxic": 1
+  },
+  "pytorch_version": "2.1.0"
+}