milesial/Pytorch-UNet
Pytorch UNet
Pytorch-UNet is a deep learning implementation designed for semantic image segmentation. It provides a framework for training convolutional neural networks to perform pixel-wise classification, transforming input images into detailed prediction masks.
The project utilizes a symmetric encoder-decoder architecture that employs skip-connection feature fusion to recover fine-grained boundary details. It includes support for mixed-precision training to reduce memory usage and accelerate processing speeds.
The framework covers the end-to-end segmentation pipeline, from model training using custom datasets to the generation of masks through a command-line tool. It also includes real-time monitoring for tracking loss curves and validation metrics, with specific applications for medical image analysis and image masking competitions.
Features
- PyTorch Semantic Segmentation Libraries - Implements a specialized PyTorch framework for semantic image segmentation using an encoder-decoder architecture.
- Computer Vision Models - Implements a convolutional neural network designed for image segmentation and object boundary detection.
- Image Segmenters - Provides a set of tools utilizing convolutional neural networks for pixel-level object isolation.
- Symmetric Encoder-Decoders - Features a symmetric encoder-decoder structure that contracts resolution for context and expands it for precise mask reconstruction.
- U-Net Architectures - Implements the U-Net architecture in PyTorch for automated semantic image segmentation.
- Skip-Connection Architectures - Utilizes skip-connections to pass high-resolution spatial information from early layers to deep layers for boundary recovery.
- Semantic Masking Architectures - Implements an architecture that assigns semantic class labels to every individual pixel for detailed image partitioning.
- Pixel-Level Classifiers - Provides a neural network architecture that treats each individual pixel as a classification target.
- Image Masking Frameworks - Provides a framework for performing pixel-level classification to isolate objects and create precise prediction masks.
- Semantic Segmentation Training - Provides a training process for semantic segmentation models using custom datasets and mixed precision.
- Deep Learning Training Toolsets - Provides a toolset for training and optimizing deep neural networks with mixed precision and real-time metric logging.
- Mixed Precision Training - Employs a combination of 16-bit and 32-bit floating point numbers to accelerate training and reduce GPU memory usage.
- Training Progress Monitoring - Includes a real-time logging system for tracking loss curves and validation metrics during model training.
- Medical Image Segmentations - Supports the isolation of anatomical structures in high-resolution medical scans through deep learning segmentation.
- Mixed Precision Training Utilities - Provides utilities for managing precision and gradient scaling to improve memory and computational efficiency during training.
- Pixel-Wise Loss Functions - Implements loss functions that calculate the difference between predicted segmentation masks and ground truth labels.
- Prediction Mask Generators - Ships a command-line tool to process images through a trained model and save the resulting semantic masks.
- Neural Network Architectures - PyTorch implementation of the U-Net architecture for segmentation.
- Image segmentation - Listed in the “Image segmentation” section of the Ailia Models awesome list.
