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+# 🧠 UNet Fibril Segmentation Model
+
+![UNet Fibril Segmentation Banner](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/model-card-image-placeholder.png)
+
+A **UNet-based deep learning model** trained for **semantic segmentation of fibrillar structures** in **single-molecule fluorescence microscopy images**. This model is specifically designed to identify and segment **amyloid fibrils**, which are critical in research related to neurodegenerative diseases such as Alzheimer’s.
+
+---
+
+## 🔬 Model Overview
+
+- **Architecture**: [UNet](https://arxiv.org/abs/1505.04597)
+- **Encoder**: ResNet34 (pretrained on ImageNet)
+- **Input Channels**: 1 (grayscale)
+- **Output**: Binary mask of fibril regions
+- **Loss Function**: BCEWithLogitsLoss / Dice Loss (combined)
+- **Framework**: PyTorch + [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch)
+
+---
+
+## 🧠 Use Case
+
+The model is built for biomedical researchers and computer vision practitioners working in:
+
+- **Neuroscience research** (e.g., Alzheimer's, Parkinson’s)
+- **Amyloid aggregation studies**
+- **Single-molecule fluorescence microscopy**
+- **Self-supervised denoising + segmentation pipelines**
+
+---
+
+## 🧪 Dataset
+
+The model was trained on a curated dataset of **fluorescence microscopy images** annotated for fibrillar structures. Images were grayscale, of size `512x512` or `256x256`, manually labeled using Fiji/ImageJ or custom annotation tools.
+
+*Note: If you're a researcher and would like to contribute more annotated data or collaborate on a dataset release, please reach out.*
+
+---
+
+## 📦 Files
+
+- `unet_fibril_seg_model.pth` — Trained PyTorch weights
+- `inference.py` — Inference script for running the model
+- `preprocessing.py` — Image normalization and transforms
+
+---
+
+## 🖼️ Example
+
+```python
+import torch
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+import segmentation_models_pytorch as smp
+
+# Load model
+model = smp.Unet(
+    encoder_name="resnet34",
+    encoder_weights="imagenet",
+    in_channels=1,
+    classes=1,
+)
+model.load_state_dict(torch.load("unet_fibril_seg_model.pth", map_location="cpu"))
+model.eval()
+
+# Load image
+img = Image.open("test_image.png").convert("L")
+transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor()
+])
+input_tensor = transform(img).unsqueeze(0)
+
+# Predict
+with torch.no_grad():
+    pred = model(input_tensor)
+    pred_mask = torch.sigmoid(pred).squeeze().numpy()
+    binary_mask = (pred_mask > 0.5).astype(np.uint8)
+
+# Save output
+Image.fromarray(binary_mask * 255).save("predicted_mask.png")