Update UNET.qmd

Toolbox/Models/UNET.qmd

@@ -45,20 +45,20 @@ U-Net has been pivotal in advancing image segmentation since its introduction in
 - **[ResUnet](https://arxiv.org/abs/1711.10684)**: Combines U-Net with residual connections for enhanced performance in complex tasks.
 
 ## Model Quick Start Guide
-### Tutorials and Getting Started Notebooks
+#### Tutorials and Getting Started Notebooks
 - **TODO**
 
-### Downloading model
+#### Downloading model
 - **TODO**
 
-### High-Level Tips for Effective Use
+#### High-Level Tips for Effective Use
 - **Pre-trained Encoders**: Consider using pre-trained encoders from models like ResNet or EfficientNet to improve performance.
 - **Regularization Techniques**: Apply dropout, early stopping, or weight decay to prevent overfitting, especially on small datasets.
 - **Data Augmentation**: Employ data augmentation techniques when working with small datasets to improve model generalization.
 - **Optimizing Loss Function**: Use specialized loss functions such as Dice coefficient or Intersection over Union (IoU) for pixel-wise optimization.
 - **Architectural Adjustments**: Depending on your dataset size, experiment with deeper or shallower architectures to balance overfitting and underfitting risks.
 
-### Related Datasets & Kaggle Challenges
+#### Related Datasets & Kaggle Challenges
 - **[Medical Decathlon Dataset](http://medicaldecathlon.com/)**: A popular benchmark dataset for biomedical image segmentation.
 - **[UW Madison GI Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation)**: Vehicle image segmentation challenge.
 - **[ISIC Skin Cancer Segmentation](https://challenge.isic-archive.com/)**: Dataset and challenges for skin lesion segmentation.