milesial/Pytorch-UNet

Health v2 · maintenance only

Adoption not part of the score

11613 stars · 2765 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch implementation of the U-Net architecture for semantic segmentation of high-resolution images, originally built for Kaggle's Carvana Image Masking Challenge. It includes training and prediction scripts, a pretrained model, and Docker support.

Use cases

• segment images into semantic classes with a U-Net in PyTorch
• train an image masking model on high definition images
• run medical image segmentation
• do portrait or multiclass segmentation
• reproduce the Carvana image masking challenge solution
• get a pretrained U-Net segmentation model

When to choose

• you need a well-tested, popular U-Net reference implementation in PyTorch
• you want a pretrained segmentation model with a high Dice score
• you want a simple training script with AMP, TensorBoard, and Weights & Biases support
• you want a Docker image to run training on GPU machines

When to avoid

• you need segmentation architectures beyond U-Net (e.g., Transformer-based models)
• you need a production inference server rather than training/prediction scripts
• your project cannot accept GPL-3.0 licensing
• you work outside PyTorch (e.g., TensorFlow or JAX)

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision deep-learning computer-vision image-processing machine-learning python cross-platform unet semantic-segmentation pytorch image-masking pretrained-model kaggle gpu docker linux

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For agents

markdown · JSON · MCP: product_card(name="milesial/Pytorch-UNet")