segmenteverygrain

At a glance

• Editor score
  6.2 / 10
• Pricing
  Open source
• Best for
  Specialized grain-segmentation research
• Paid from
  Free
• Model-assisted labeling
  Yes
• Facts checked
  24 Sep 2026

• Where it wins

  • Combines U-Net and SAM 2.1 for model-assisted grain segmentation
  • Supports interactive edits, measurement, calibration, and classification
  • Exports grain boundaries, measurements, masks, and labeled images

• Where it doesn't

  • Focused on grains and grain-like objects, not general image annotation
  • Self-hosted deployment requires running the Python package locally
  • The documented image formats are limited to JPEG and PNG

Our verdict on segmenteverygrain

segmenteverygrain is an open-source Python package for detecting, editing, measuring, and classifying grains or grain-like objects in images. It is aimed at geomorphology and sedimentary-geology workflows, making it a specialized option for researchers who need grain-level segmentation rather than a general-purpose image annotation system. Its workflow combines a U-Net first pass with SAM 2.1, followed by interactive tools for creating, deleting, merging, and editing grain regions. Annotation includes instance segmentation masks, polygon editing, point prompts, and grain classification labels.

The package goes beyond drawing boundaries: it supports scale-bar calibration, grain size distributions, individual grain extraction, feature extraction, clustering, and interactive classification. Large images can be processed in patches with resumable checkpoints, and users can fine-tune or train U-Net models with custom masks. Export options include GeoJSON, CSV, PNG or JPEG masks, and a labeled-image directory. That breadth suits studies where segmentation is only one step in a measurement and classification workflow. The tradeoff is its narrow focus: teams annotating unrelated image types may find a general image annotation tool a better fit.

segmenteverygrain is distributed as an Apache-2.0 Python package, with Windows, macOS, and Linux listed as platforms. Deployment is self-hosted, so it is not presented as a hosted annotation service. Documentation and community are the listed support channels; the package also lists API access. These characteristics make it a plausible fit for solo researchers and small teams comfortable with a Python package and local deployment. Choose it when grain-specific analysis, model-assisted segmentation, and research exports matter; look elsewhere if you need a broad annotation platform or hosted deployment.

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segmenteverygrain fact sheet

Alternatives to segmenteverygrain

• PPixanoA flexible open-source choice for teams annotating 2D and 3D vision datasets.9.0
• CVATA broad CV workflow spanning manual, AI-assisted, collaborative, and programmatic annotation.8.9
• Label StudioFlexible image annotation with open-source deployment, AI assistance, and broad exports.8.8

See all segmenteverygrain alternatives →

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update