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ZrrSkywalker/Personalize-SAM

Personalize Segment Anything Model (SAM) with 1 shot in 10 seconds observed · 2026-08-28

github.com/ZrrSkywalker/Personalize-SAM · Python · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

29/100

  • Activity 0
  • Release rhythm 35
  • Longevity 86

Flags: no_releases

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 1217
  • days_rel: n/a
  • days_push: 772
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1671 stars · 113 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PerSAM is the official implementation of 'Personalize Segment Anything Model with One Shot', which customizes the Segment Anything Model (SAM) to segment specific visual concepts (e.g., a pet dog) in other images or videos given a single reference image with a mask. It offers a training-free variant and a fine-tuned variant (PerSAM-F) that trains only 2 parameters in 10 seconds.

Use cases

  • segment my pet dog across a whole photo album from one labeled example
  • personalize SAM for one-shot object segmentation without training
  • segment a specific object in videos given a single reference mask
  • remove background disturbance for DreamBooth fine-tuning of Stable Diffusion
  • fine-tune SAM on my own dataset with a single shot in seconds
  • run a web demo of personalized segmentation

When to choose

  • you need to segment a specific object instance across many images or videos with only one annotated example
  • you want a training-free or near-instant (10-second) SAM personalization
  • you want to clean up few-shot training images for DreamBooth/Stable Diffusion personalization
  • you need efficient segmentation on resource-constrained setups via MobileSAM support

When to avoid

  • you need general-purpose segmentation of arbitrary objects without a reference mask
  • you need multi-shot or large-scale supervised segmentation training
  • you need a production-ready application with a polished UI rather than a research codebase
  • you work outside Python/GPU environments

Facets

library · maturity active

machine-learning computer-vision image-processing computer-vision image-processing deep-learning artificial-intelligence python segment-anything sam one-shot-segmentation personalized-segmentation persam image-segmentation training-free stable-diffusion-assist gpu

1 source

Member repositories

RepositoryRoleHealth v2
ZrrSkywalker/Personalize-SAMmain29

For agents

markdown · JSON · MCP: product_card(name="ZrrSkywalker/Personalize-SAM")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem