qubvel/segmentation_models
Segmentation Models
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.
- Mask Overlap Optimization - Applies specialized loss functions to refine the overlap of predicted masks against ground truth data.
- Backbone Model Integration - Integrates pre-trained convolutional backbones to identify and isolate complex patterns in image data.
- Vision Loss Functions - Implements specialized vision loss functions that measure mask overlap to improve model accuracy.
- Transfer Learning - Leverages pre-trained weights from image classification networks to accelerate training of segmentation models.
- Semantic Segmentation Architectures - Implements encoder-decoder architectures specifically for pixel-wise semantic segmentation.
- Computer Vision Toolkits - Offers a toolkit of overlap-based loss functions and precision metrics for evaluating segmentation accuracy.
- Feature Map Upsamplers - Increases spatial resolution of feature maps using transposed convolutions to align predictions with original image size.
- Image Data Preprocessing - Prepares raw image data to ensure compatibility between data sources and model encoders.
- Instance Segmentation Loss Calculators - Computes specialized loss functions to determine the exact accuracy of generated image masks.
- Segmentation Model Abstractions - Wraps deep learning layers into high-level API classes for rapid segmentation network assembly.
- Segmentation Evaluation Metrics - Calculates overlap and precision metrics to evaluate how well the model identifies object boundaries.
- Image Preprocessing Pipelines - Includes workflows for normalizing image pixel values for compatibility with chosen encoders.
- Input Normalizers - Provides utilities for standardizing image pixel values to match pre-trained encoder distributions.
