Update README.md

README.md

@@ -18,35 +18,32 @@ tags:
 - wheat
 ---
 
-# 🌾 HeadCount
+# HeadCount
 
-A semantic segmentation model for counting wheat heads in field images. Designed for yield estimation, flowering time detection, and field maturity assessment.
+A semantic segmentation model for counting wheat heads in field images, designed for yield estimation and field maturity assessment.
 
 **[Interactive Demo →](https://huggingface.co/spaces/chmcbs/HeadCount)**
 
 **[GitHub Repo →](https://github.com/chmcbs/HeadCount)**
 
 ## 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
+- **Loss Function:** Dice loss with inverse frequency weighting (1.5× stem class weight)
+- **Optimisation:** Adam with CosineAnnealingLR learning-rate scheduling
+- **Counting:** Distance transform + peak detection on head mask
 
 ## Performance
-
 | Class      | F1    |
 |------------|-------|
-| Background | 0.858 |
+| Head       | 0.897 |
 | Leaf       | 0.889 |
+| Background | 0.858 |
 | Stem       | 0.535 |
-| Head       | 0.897 |
 
 ## Example Usage
-
 ```python
 from inference import GWFSSModel
 from PIL import Image
@@ -54,26 +51,24 @@ from PIL import Image
 # Load model
 model = GWFSSModel("model.pth")
 
-# Load and process image
+# 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")
+print(f"Detected {num_heads} heads")
 
-# Create visualisation
+# Save overlay
 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
+- **Lighting Sensitivity:** Harsh lighting can cause over-segmentation, splitting heads into multiple detections
 - **Overlapping Heads:** Dense clusters with significant overlap are challenging to separate accurately
-- **Colour Dependency:** Performance is lower on senesced plants
+- **Colour Dependency:** Performance is worse on senesced plants due to limited training examples
 
 ## Training Data
-
 This model is 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.