Update README.md

README.md

@@ -35,8 +35,66 @@ size_categories:
 - **Multi-class Classification**: Classify building damage severity into 5 ordinal levels
 - **Damage Assessment**: Support automated post-disaster reconnaissance and response
 
-### Languages
-Annotations are in English. Image content is geographic (building structures, damage patterns).
+---
+
+## Quick Start
+
+### Loading the Dataset
+```python
+from datasets import load_dataset
+
+# Load full dataset
+dataset = load_dataset("crumeike/tornadonet-datasets")
+
+# Access specific split
+train_data = dataset["train"]
+val_data = dataset["validation"]
+test_data = dataset["test"]
+```
+
+### Using with YOLO
+```python
+# Dataset is already in YOLO format
+# Annotation format: <class> <x_center> <y_center> <width> <height>
+
+# Example YOLO configuration (data.yaml)
+"""
+path: /path/to/tornadonet
+train: images/train
+val: images/val
+test: images/test
+
+nc: 5  # number of classes
+names: ['DS0_Undamaged', 'DS1_Slight', 'DS2_Moderate', 'DS3_Extensive', 'DS4_Complete']
+"""
+```
+
+### Evaluation Metrics
+For ordinal classification tasks, consider using:
+- **Standard Metrics**: mAP@0.5, F1-score, Precision, Recall
+- **Ordinal Metrics**: 
+  - Ordinal Top-k Accuracy
+  - Mean Absolute Ordinal Error (MAOE)
+  - Confusion matrices emphasizing near-miss errors
+
+### Baseline Models
+Benchmark results on TornadoNet:
+
+| Model | mAP@0.5 | F1 Score | Ordinal Top-1 Acc | MAOE |
+|-------|---------|----------|-------------------|------|
+| YOLOv8n | 40.98% | 45.11% | 84.01% | 0.78 |
+| YOLOv8l | 42.09% | 46.41% | 84.19% | 0.78 |
+| YOLO11x | 46.05% | 49.40% | 85.20% | 0.76 |
+| RT-DETR-L | 39.87% | 44.77% | 88.13% | 0.65 |
+
+###  Ordinal Supervision Impact
+| Model | Configuration | mAP@0.5 | Δ vs Baseline | Ordinal Top-1 | MAOE |
+|-------|---------------|---------|---------------|---------------|------|
+| RT-DETR-L | ψ=0.5, K=1 | **44.70%** | **+4.8 pp** | **91.15%** | **0.56** |
+
+*See [full paper](https://arxiv.org/abs/2603.11557) for detailed experimental results and analysis.*
+
+---
 
 ## Dataset Structure
 
@@ -137,66 +195,6 @@ The dataset contains street-view imagery of buildings in disaster-affected areas
 - Geographic metadata is included for research purposes
 - Researchers should use appropriate care when publishing derived visualizations
 
----
-
-## Quick Start
-
-### Loading the Dataset
-```python
-from datasets import load_dataset
-
-# Load full dataset
-dataset = load_dataset("crumeike/tornadonet-datasets")
-
-# Access specific split
-train_data = dataset["train"]
-val_data = dataset["validation"]
-test_data = dataset["test"]
-```
-
-### Using with YOLO
-```python
-# Dataset is already in YOLO format
-# Annotation format: <class> <x_center> <y_center> <width> <height>
-
-# Example YOLO configuration (data.yaml)
-"""
-path: /path/to/tornadonet
-train: images/train
-val: images/val
-test: images/test
-
-nc: 5  # number of classes
-names: ['DS0_Undamaged', 'DS1_Slight', 'DS2_Moderate', 'DS3_Extensive', 'DS4_Complete']
-"""
-```
-
-### Evaluation Metrics
-For ordinal classification tasks, consider using:
-- **Standard Metrics**: mAP@0.5, F1-score, Precision, Recall
-- **Ordinal Metrics**: 
-  - Ordinal Top-k Accuracy
-  - Mean Absolute Ordinal Error (MAOE)
-  - Confusion matrices emphasizing near-miss errors
-
-### Baseline Models
-Benchmark results on TornadoNet:
-
-| Model | mAP@0.5 | F1 Score | Ordinal Top-1 Acc | MAOE |
-|-------|---------|----------|-------------------|------|
-| YOLOv8n | 40.98% | 45.11% | 84.01% | 0.78 |
-| YOLOv8l | 42.09% | 46.41% | 84.19% | 0.78 |
-| YOLO11x | 46.05% | 49.40% | 85.20% | 0.76 |
-| RT-DETR-L | 39.87% | 44.77% | 88.13% | 0.65 |
-
-###  Ordinal Supervision Impact
-| Model | Configuration | mAP@0.5 | Δ vs Baseline | Ordinal Top-1 | MAOE |
-|-------|---------------|---------|---------------|---------------|------|
-| RT-DETR-L | ψ=0.5, K=1 | **44.70%** | **+4.8 pp** | **91.15%** | **0.56** |
-
-*See [full paper](https://arxiv.org/abs/2603.11557) for detailed experimental results and analysis.*
-
----
 
 ## Additional Information