lovely-pancake 1.0.5

My lovely python package

LovelyPancake

LovelyPancake is a general-purpose Python package for machine learning, starting with a robust set of tools for image segmentation. It currently features implementations of U-Net-based models with plans to extend into other machine learning domains.

Installation

To install LovelyPancake, run:

pip install lovelypancake

Requirements

The package requires Python 3.x and TensorFlow 2.x. Install all dependencies with:

pip install -r requirements.txt

Current Features

• U-Net model architecture for image segmentation tasks.
• Attention U-Net and Attention Residual U-Net for advanced segmentation needs.
• Modular design for easy customization and extension of model components.
• Pre-built loss functions and metrics commonly used in image segmentation.

Future Scope

• Expansion to include a wide range of machine learning models beyond image segmentation.
• Utility functions for data preprocessing, augmentation, and evaluation metrics.
• Integration with other machine learning frameworks and tools.

Usage

To create and use a U-Net model:

from lovelypancake.models import unet

model = unet(input_shape=(256, 256, 3), NUM_CLASSES=2, dropout_rate=0.1, batch_norm=True)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

For detailed examples and more advanced usage, see the Documentation [Under Progres...].