Update model files

CITATION.cff

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+cff-version: 1.2.0
+message: "If you use this model or code, please cite as below."
+title: "Gray Leaf Spot Segmentation Model"
+authors:
+  - family-names: "Rohan"
+    given-names: "R."
+    orcid: "https://orcid.org/0009-0005-9225-1775"
+year: 2026

LICENSE

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+   Copyright [2026] [Rohan R]
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README.md

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----
-license: mit
+license: apache-2.0
 library_name: pytorch
 pipeline_tag: image-segmentation
 tags:
 - image-segmentation
 - pytorch
 - unet
----
 
 # Gray Leaf Spot Segmentation Model
 
 PyTorch U-Net segmentation model trained for gray leaf spot segmentation.
 
-## Files
 
-- `grayleafspot.pt`: serialized model artifact
+
+
+- `grayleafspot.pt`: main U-Net model artifact
+- `best_area_w_0.1.pt`: area consistency U-Net (weight=0.1)
+- `best_area_w_0.3.pt`: area consistency U-Net (weight=0.3)
+- `best_area_w_0.5.pt`: area consistency U-Net (weight=0.5)
+- `best_area_w_0.7.pt`: area consistency U-Net (weight=0.7)
 - `requirements.txt`: minimal runtime dependencies
 
 ## Usage
@@ -26,6 +29,78 @@ model = torch.load("grayleafspot.pt", map_location="cpu")
 model.eval()
 ```
 
+## Training & Inference Pipeline
+
+This repository follows the TrainPetri pipeline for image segmentation, adapted for gray leaf spot. Key steps:
+
+### Environment Setup (Apple Silicon recommended)
+
+1. Create and activate a Python 3.10+ virtual environment:
+	 ```bash
+	 python3.10 -m venv trainenv
+	 source trainenv/bin/activate
+	 pip install --upgrade pip
+	 pip install -r requirements.txt
+	 ```
+
+### Dataset Preparation
+
+- Place your raw images in `raw/` and corresponding masks in `masks/` (matching filenames).
+- To expand the dataset with augmentations:
+	```bash
+	python src/build_augmented_dataset.py --copies-per-image 4 --clean
+	```
+
+### Training
+
+- Standard U-Net training:
+	```bash
+	python src/train.py --image-dir augmented_dataset/raw --mask-dir augmented_dataset/masks --epochs 40 --batch-size 4 --lr 1e-4 --image-size 256 --freeze-encoder-epochs 5
+	```
+- Area consistency U-Net (LabelMe JSON polygons):
+	```bash
+	./trainenv/bin/python src/area_consistency/train_area.py
+	```
+
+### Inference
+
+- On a single image:
+	```bash
+	python src/predict.py --input raw/your_image.jpg --weights models/best_finetuned.pt --output-dir predictions
+	```
+- On a folder:
+	```bash
+	python src/predict.py --input raw --weights models/best_finetuned.pt --output-dir predictions
+	```
+
+### Best Practices
+
+- Always keep your trusted human-label masks unchanged.
+- Use augmentations and area consistency loss for improved generalization.
+- Inspect overlay outputs to verify mask quality.
+- For Apple Silicon, MPS acceleration is used automatically if available.
+
+
+
+
+## Citation
+
+If you use this model or code, please cite:
+
+```
+@software{rohan_grayleafspot_2026,
+	author = {Rohan, R.},
+	title = {Gray Leaf Spot Segmentation Model},
+	year = {2026},
+	orcid = {0009-0005-9225-1775}
+}
+```
+
+
+-## License
+
+This project is licensed under the Apache License 2.0. See the LICENSE file for details.
+
 ## Access
 
 This repository is gated with manual approval enabled on the Hugging Face Hub.

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