Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: mit
+license: cc-by-4.0
 datasets:
 - GlobalWheat/GWFSS_v1.0
 metrics:
@@ -10,10 +10,67 @@ pipeline_tag: image-segmentation
 tags:
 - computer-vision
 - semantic-segmentation
-- wheat
-- agriculture
+- instance-counting
 - phenotyping
+- agriculture
 - crop-analysis
-- instance-counting
 - yield-estimation
----
+- wheat
+---
+
+# 🌾 HeadCount: Automated Wheat Head Counter
+
+Semantic segmentation model for counting wheat heads in field images. Designed for yield estimation from aerial imagery, flowering time detection, and field maturity assessment.
+
+## Model Details
+
+- **Architecture:** DeepLabV3+ with ResNet50 encoder
+- **Framework:** PyTorch with segmentation-models-pytorch
+- **Input:** RGB images (resized to 512×512)
+- **Output:** 4-class segmentation (Background, Leaf, Stem, Head)
+- **Counting Method:** Distance transform + peak detection on head mask
+- **Loss Function:** Dice loss with inverse frequency weighting (1.5× stem boost)
+- **Optimizer:** Adam with CosineAnnealingLR scheduling
+
+## Performance
+
+| Class      | F1    |
+|------------|-------|
+| Background | 0.858 |
+| Leaf       | 0.889 |
+| Stem       | 0.535 |
+| Head       | 0.897 |
+
+## Example Usage
+
+```python
+from inference import GWFSSModel
+from PIL import Image
+
+# Load model
+model = GWFSSModel("model.pth")
+
+# Load and process image
+image = Image.open("input.jpg")
+predictions = model.predict(image)
+
+# Count heads
+num_heads = model.count_heads(predictions)
+print(f"🌾 {num_heads} heads detected!")
+
+# Create visualisation
+overlay = model.overlay_mask(image, predictions, alpha=0.5, heads_only=True)
+overlay.save("output.png")
+```
+
+## Limitations
+
+Best performance is achieved with overhead imagery under diffuse lighting. Known challenges include:
+- **Lighting Sensitivity:** Bright or harsh lighting can cause over-segmentation, splitting single heads into multiple detections
+- **Overlapping Heads:** Dense clusters with significant overlap are challenging to separate accurately
+- **Colour Dependency:** Performance is lower on senesced plants
+
+## Training Data
+
+This model was trained on [`GWFSS_v1.0_labelled`](https://huggingface.co/datasets/GlobalWheat/GWFSS_v1.0) from the 
+[Global Wheat Full Semantic Organ Segmentation](https://doi.org/10.1016/j.plaphe.2025.100084) dataset.