dbolya/yolact

Health v2 · maintenance only

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Adoption not part of the score

5241 stars · 1323 forks observed · 2026-08-28

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

YOLACT is a PyTorch implementation of a fully convolutional model for real-time instance segmentation, accompanying the YOLACT and YOLACT++ research papers. It provides training and evaluation on COCO, pretrained models, and real-time inference at ~30 fps on a Titan Xp.

Use cases

• run real-time instance segmentation on images or video
• segment individual objects in a webcam feed
• train an instance segmentation model on COCO
• evaluate a segmentation model on COCO test-dev
• use pretrained YOLACT++ weights for object masks
• benchmark real-time segmentation fps on GPU

When to choose

• you need fast, real-time instance segmentation with per-object masks
• you want a simple, fully convolutional segmentation architecture to study or extend
• you need pretrained COCO models with a permissive MIT license

When to avoid

• you need state-of-the-art accuracy rather than speed (newer transformers outperform it)
• you need a maintained production library with easy pip installation
• you cannot compile CUDA extensions like DCNv2 for YOLACT++
• you need semantic or panoptic segmentation instead of instance segmentation

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning image-processing python cross-platform instance-segmentation real-time pytorch yolact object-detection coco research gpu linux

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