Create README.md

README.md

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+---
+license: cc-by-nc-4.0
+library_name: pytorch
+pipeline_tag: image-segmentation
+tags:
+  - pytorch
+  - medical-imaging
+  - image-segmentation
+  - semantic-segmentation
+  - wound-segmentation
+  - diabetic-foot-ulcer
+  - dfu
+  - unet
+  - unet-plus-plus
+  - efficientnet
+  - deep-learning
+  - computer-vision
+  - healthcare
+  - medical-ai
+  - fuseg
+  - miccai
+datasets:
+  - custom
+metrics:
+  - iou
+  - dice
+---
+
+# DiaFoot.AI 🦶
+
+**State-of-the-Art Diabetic Foot Ulcer Segmentation**
+
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/Ruthvik-Bandari/DiaFoot.AI)
+[![License](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](LICENSE)
+
+## Model Description
+
+DiaFoot.AI is a deep learning model for automatic segmentation of diabetic foot ulcers from clinical images. It uses a U-Net++ architecture with an EfficientNet-B4 encoder pretrained on ImageNet.
+
+### Performance
+
+| Metric | DiaFoot.AI | FUSeg 2021 Winner | Improvement |
+|--------|-----------|-------------------|-------------|
+| **IoU** | **85.58%** | 80.30% | **+5.28%** |
+| **Dice** | **92.23%** | 89.23% | **+3.00%** |
+
+### Cross-Validation Results
+
+| Fold | IoU | Dice |
+|------|-----|------|
+| Fold 1 | 83.22% | 90.84% |
+| Fold 2 | 85.81% | 92.36% |
+| Fold 3 | 82.16% | 90.20% |
+| Fold 4 | 85.00% | 91.89% |
+| Fold 5 | 84.44% | 91.56% |
+| **Mean** | **84.13% ± 1.30%** | **91.37% ± 0.77%** |
+
+## Usage
+
+### Installation
+```bash
+pip install torch segmentation-models-pytorch albumentations huggingface_hub
+```
+
+### Download and Load Model
+```python
+from huggingface_hub import hf_hub_download
+import torch
+import segmentation_models_pytorch as smp
+
+# Download model
+model_path = hf_hub_download("RuthvikBandari/DiaFootAI", "best_model.pt")
+
+# Create model architecture
+model = smp.UnetPlusPlus(
+    encoder_name="efficientnet-b4",
+    encoder_weights=None,
+    in_channels=3,
+    classes=1
+)
+
+# Load weights
+checkpoint = torch.load(model_path, map_location="cpu", weights_only=False)
+state_dict = checkpoint["model_state_dict"]
+# Remove 'model.' prefix if present
+state_dict = {k.replace("model.", "", 1): v for k, v in state_dict.items()}
+model.load_state_dict(state_dict)
+model.eval()
+```
+
+### Inference
+```python
+import cv2
+import numpy as np
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+
+# Preprocessing
+transform = A.Compose([
+    A.LongestMaxSize(max_size=512),
+    A.PadIfNeeded(min_height=512, min_width=512, border_mode=cv2.BORDER_CONSTANT),
+    A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ToTensorV2()
+])
+
+# Load and preprocess image
+image = cv2.imread("wound_image.jpg")
+image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+transformed = transform(image=image)
+input_tensor = transformed["image"].unsqueeze(0)
+
+# Predict
+with torch.no_grad():
+    output = model(input_tensor)
+    mask = torch.sigmoid(output).squeeze().numpy()
+    binary_mask = (mask > 0.5).astype(np.uint8)
+```
+
+## Model Architecture
+
+- **Architecture:** U-Net++
+- **Encoder:** EfficientNet-B4 (ImageNet pretrained)
+- **Input Size:** 512 × 512 × 3
+- **Output:** Single channel probability map
+- **Parameters:** 20.8M
+
+## Training Details
+
+- **Loss:** Focal Tversky + BCE with warmup
+- **Optimizer:** AdamW
+- **Learning Rate:** 3e-4 (decoder), 1e-5 (encoder)
+- **Batch Size:** 8
+- **Epochs:** 100
+- **Augmentation:** Heavy (flips, rotations, color jitter, elastic transform)
+- **Test Time Augmentation:** 8 transforms
+
+## Dataset
+
+Trained on [FUSeg 2021 Challenge Dataset](https://github.com/uwm-bigdata/wound-segmentation):
+- 810 training images
+- 200 validation images
+- Pixel-wise annotations by wound care experts
+
+## Citation
+```bibtex
+@software{bandari2025diafootai,
+  author = {Bandari, Ruthvik},
+  title = {DiaFoot.AI: Deep Learning for Diabetic Foot Ulcer Segmentation},
+  year = {2025},
+  publisher = {GitHub},
+  url = {https://github.com/Ruthvik-Bandari/DiaFoot.AI},
+  note = {Achieves 85.58% IoU, surpassing MICCAI FUSeg 2021 winner by +5.28%}
+}
+```
+
+## License
+
+**CC BY-NC 4.0** - You must give appropriate credit and may not use for commercial purposes without permission.
+
+## Author
+
+**Ruthvik Bandari**  
+Master's in Applied Artificial Intelligence  
+Northeastern University
+
+- GitHub: [@Ruthvik-Bandari](https://github.com/Ruthvik-Bandari)
+- LinkedIn: [Ruthvik Bandari](https://linkedin.com/in/ruthvik-bandari)
+
+## Disclaimer
+
+This model is intended for research purposes only. Not for clinical diagnosis without professional medical oversight.