# milesial/pytorch-unet **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/milesial-pytorch-unet).** 11,503 stars · 2,752 forks · Python · GPL-3.0 ## Links - GitHub: https://github.com/milesial/Pytorch-UNet - awesome-repositories: https://awesome-repositories.com/repository/milesial-pytorch-unet.md ## Description 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. ## Tags ### Artificial Intelligence & ML - [PyTorch Semantic Segmentation Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-semantic-segmentation-libraries.md) — Implements a specialized PyTorch framework for semantic image segmentation using an encoder-decoder architecture. - [Computer Vision Models](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-models.md) — Implements a convolutional neural network designed for image segmentation and object boundary detection. - [Image Segmenters](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks/image-segmenters.md) — Provides a set of tools utilizing convolutional neural networks for pixel-level object isolation. - [Symmetric Encoder-Decoders](https://awesome-repositories.com/f/artificial-intelligence-ml/encoder-decoder-architectures/symmetric-encoder-decoders.md) — Features a symmetric encoder-decoder structure that contracts resolution for context and expands it for precise mask reconstruction. - [U-Net Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-architectures/u-net-architectures.md) — Implements the U-Net architecture in PyTorch for automated semantic image segmentation. - [Skip-Connection Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/skip-connection-architectures.md) — Utilizes skip-connections to pass high-resolution spatial information from early layers to deep layers for boundary recovery. - [Semantic Segmentation Training](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/image-segmentation/segmentation-model-training/semantic-segmentation-training.md) — Provides a training process for semantic segmentation models using custom datasets and mixed precision. ([source](https://github.com/milesial/pytorch-unet#readme)) - [Deep Learning Training Toolsets](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-training-toolsets.md) — Provides a toolset for training and optimizing deep neural networks with mixed precision and real-time metric logging. - [Mixed Precision Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/mixed-precision-training.md) — Employs a combination of 16-bit and 32-bit floating point numbers to accelerate training and reduce GPU memory usage. - [Training Progress Monitoring](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/utilities/training-progress-monitoring.md) — Includes a real-time logging system for tracking loss curves and validation metrics during model training. ([source](https://github.com/milesial/pytorch-unet#readme)) - [Medical Image Segmentations](https://awesome-repositories.com/f/artificial-intelligence-ml/medical-image-segmentations.md) — Supports the isolation of anatomical structures in high-resolution medical scans through deep learning segmentation. - [Mixed Precision Training Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/mixed-precision-training-utilities.md) — Provides utilities for managing precision and gradient scaling to improve memory and computational efficiency during training. - [Pixel-Wise Loss Functions](https://awesome-repositories.com/f/artificial-intelligence-ml/pixel-wise-loss-functions.md) — Implements loss functions that calculate the difference between predicted segmentation masks and ground truth labels. - [Prediction Mask Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/prediction-mask-generators.md) — Ships a command-line tool to process images through a trained model and save the resulting semantic masks. ([source](https://github.com/milesial/pytorch-unet#readme)) ### Data & Databases - [Semantic Masking Architectures](https://awesome-repositories.com/f/data-databases/dataset-class-mappers/pixel-class-predictors/semantic-masking-architectures.md) — Implements an architecture that assigns semantic class labels to every individual pixel for detailed image partitioning. ### Education & Learning Resources - [Pixel-Level Classifiers](https://awesome-repositories.com/f/education-learning-resources/neural-network-tutorials/binary-classification-models/pixel-level-classifiers.md) — Provides a neural network architecture that treats each individual pixel as a classification target. ([source](https://github.com/milesial/pytorch-unet#readme)) ### Graphics & Multimedia - [Image Masking Frameworks](https://awesome-repositories.com/f/graphics-multimedia/image-masking-frameworks.md) — Provides a framework for performing pixel-level classification to isolate objects and create precise prediction masks. ### Part of an Awesome List - [Neural Network Architectures](https://awesome-repositories.com/f/awesome-lists/ai/neural-network-architectures.md) — PyTorch implementation of the U-Net architecture for segmentation. - [Image segmentation](https://awesome-repositories.com/f/awesome-lists/more/image-segmentation.md) — Listed in the “Image segmentation” section of the Ailia Models awesome list.