Segmentation Models | Awesome Repository

This is an image segmentation framework and masking toolkit for constructing binary and multi-class neural network architectures. It serves as a deep learning encoder wrapper that integrates pre-trained convolutional neural network architectures into semantic segmentation models.

The library enables the use of pre-trained backbones to isolate complex patterns and leverages transfer learning to accelerate training. It provides a collection of overlap-based loss functions and precision metrics specifically designed to evaluate and refine the accuracy of image masks.

The toolkit covers the full segmentation pipeline, including image input normalization, the assembly of encoder-decoder architectures, and the calculation of performance scores using overlap and precision metrics.

Features

Image Segmentation - Provides a framework for constructing neural networks that isolate specific objects within images.
PyTorch Semantic Segmentation Libraries - Provides a comprehensive framework for building semantic segmentation models using pre-trained encoders and specialized loss functions.
Deep Learning Architectures - Provides an interface for integrating pre-trained CNN architectures into semantic segmentation models.
Overlap-Based - Provides Tversky-based loss functions to handle class imbalance between target objects and background pixels.

Features

Image Segmentation - Provides a framework for constructing neural networks that isolate specific objects within images.
PyTorch Semantic Segmentation Libraries - Provides a comprehensive framework for building semantic segmentation models using pre-trained encoders and specialized loss functions.
Deep Learning Architectures - Provides an interface for integrating pre-trained CNN architectures into semantic segmentation models.
Overlap-Based - Provides Tversky-based loss functions to handle class imbalance between target objects and background pixels.