Labelme
Offline AI image annotation with SAM2 & SAM3, one-time price, no subscription
A smart one-time purchase for privacy-focused computer vision teams. The built-in offline AI models rival cloud tools, but lack of real-time collaboration means it's not for large distributed teams. Choose LabelMe if you need data sovereignty and permanent ownership; consider Supervisely or Roboflow if you need team collaboration and cloud APIs.
Verified 5d ago · liveness 55/100 · cite: rightaichoice.com/tools/labelme
- Computer vision researchers needing private, offline annotation
- Engineers building custom YOLO training datasets with oriented boxes
- Teams in regulated industries (medical, satellite) requiring data sovereignty
- Solo practitioners who prefer one-time purchase over subscription
- Cloud-centric teams needing multi-user real-time collaboration
- Users expecting a web-based or mobile annotation tool
- Those requiring API access for programmatic automation
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Skip LabelMe if you need real-time multi-user collaboration or a cloud API, since it's strictly offline and single-user per license.
Going past 1 year of updates on Starter or Pro means paying again to stay current; only Lifetime and Team include updates forever.
The one-time pricing fits solo practitioners and small teams who want to avoid recurring fees; cheaper than subscription tools like Roboflow over 2+ years, but cloud tools offer collaboration and APIs.
In short
Labelme — Offline AI image annotation with SAM2 & SAM3, one-time price, no subscription. Best for Computer vision researchers needing private, offline annotation, Engineers building custom YOLO training datasets with oriented boxes, Teams in regulated industries (medical, satellite) requiring data sovereignty. Free to start; paid plans from $49/mo.
What's new in Labelme
Checked 5 days agoAcross the latest 4 updates: 2 feature updates and 2 changelog entries.
AI Assist no longer crashes on overlapping shapes
v7.0.4 fixes an AI Assist / AI Box crash on older Pillow versions when a new detection overlapped existing shapes.
Deleting from the annotation list no longer crashes your next shape
v7.0.3 fixes a crash when deleting annotations from the list and stops AI polygon output from choking on thin detections.
Dark mode, and a theme that follows your system
v7.0 adds a color theme setting with System, Light, and Dark. Dark mode is new, and the switch applies live without restart.
Annotate past the edge of the image
BETA preview: place polygon and shape points beyond the image boundary to preserve true geometry for off-frame objects.
What people actually say about Labelme — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
- +Fully offline AI-assisted annotation with SAM and YOLO-World.
- +Lifetime license pricing, no recurring subscriptions.
- +Supports polygon, rectangle, circle, line, point, oriented rectangle, mask.
- +Multi-gigapixel and TIFF image support in v6.0+.
- +Export to YOLO, VOC, YOLO-OBB formats out of the box.
- −Installation requires Python or Docker knowledge; not beginner-friendly.
- −No built-in cloud backup or team collaboration features.
- −macOS version has stability issues with large images.
- −Customer support slow even on paid plans.
- −Free version lacks CLI batch processing toolkit.
- • No free trial for paid tiers; paid tiers charge one-time, but upgrades cost extra.
- • AI models (SAM, YOLO) may require additional disk space download.
Viability Score
How well maintained and how widely used is Labelme? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Offline AI annotation with SAM2, SAM3, YOLO-World (no internet/API)
- Click-to-segment: one-click polygon or mask via EfficientSAM, SAM1, SAM2
- Text-prompt annotation: type 'person, shoe, bus' to auto-annotate
- Shape types: polygon, rectangle, circle, line, point, oriented rectangle
- Annotate past edge of image (beta preview)
- On-canvas label display (beta preview)
- Multi-gigapixel and float32 GeoTIFF support
- Dark mode and system theme (v7.0)
- Settings dialog with live updates (v7.0)
- 14 languages including English, 中文, Français, 日本語, etc.
- YOLO-OBB export/import via Toolkit (v0.3.0)
- Batch processing and automation via CLI Toolkit
- Image classification flags (cat: true/false)
- Multi-select labels with range-select and hide/show
- AI output shapes: polygon, mask, rectangle, oriented rectangle, circle
About Labelme
LabelMe is a desktop image annotation tool for computer vision professionals who need private, offline dataset creation. It bundles built-in AI models (SAM2, SAM3, YOLO-World, EfficientSAM, SAM1) for click-to-segment and text-prompt annotation that runs entirely on your machine without an internet connection or API key. The paid app (Starter, Pro, Pro Lifetime, Team) provides one-click setup with a standalone installer, while the free open-source version requires Python installation. Key features include polygon, rectangle, circle, line, point, and oriented rectangle shapes; multi-gigapixel and TIFF support; 14 languages; and exports to YOLO, VOC, and YOLO-OBB via the Pro Toolkit. Recent v7.0 updates add dark mode, on-canvas label display, and annotations past the image edge. Compared to cloud-based alternatives like Supervisely or Roboflow, LabelMe prioritizes data sovereignty and one-time pricing over collaboration—ideal for regulated industries (medical, satellite) or solo practitioners who want a permanent tool without recurring fees.
Behind the Verdict
LabelMe stands out as a genuinely offline, one-time-purchase annotation tool, which is rare in a market dominated by subscription cloud platforms. The built-in AI models (SAM2, SAM3, YOLO-World) let you annotate thousands of images in an afternoon without uploading data or paying per image. The app is fast to set up (download and double-click), and the free open-source version offers a no-cost entry point if you're comfortable with Python. Strengths: True data sovereignty—your data never leaves your machine. No recurring fees, so it's cheap over time (Pro Lifetime pays for itself in ~3 years). The toolkit (Pro) provides batch processing, automation, and exports to YOLO, VOC, and YOLO-OBB, which directly feeds into training pipelines. Recent v7.0 updates (dark mode, canvas labels, past-edge annotation) show active development. Weaknesses: No cloud collaboration, so teams needing real-time multi-user work will be disappointed. The free version requires Python setup, and the paid app's AI features are gated behind paid tiers. Updates are limited to 1 year on Starter and Pro (unless you buy Lifetime). Where it fits: Solo practitioners, small teams in regulated industries (medical, satellite), and anyone needing offline AI-assisted annotation without GPU or cloud credits. Where it doesn't: Cloud-centric teams needing multi-user collaboration or API access. Overall, if privacy and cost predictability are your priorities, LabelMe is a compelling choice.
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Real-world workflow fit
Concrete scenarios for the personas Labelme actually fits — and what changes day-one when you adopt it.
Need to create a custom YOLOv8 dataset offline from satellite imagery.
Outcome: Install LabelMe Pro, use text-prompt annotation (YOLO-World) to auto-label objects, then export to YOLO format via toolkit and train with Ultralytics — all without uploading data.
Annotate medical images without cloud upload to comply with regulations.
Outcome: Use LabelMe's offline AI (SAM2/SAM3) to segment structures, with multi-gigapixel TIFF support for large scans, and keep all data on your machine.
Annotate hundreds of similar-shaped objects (e.g., fruit) for a client.
Outcome: Use AI click-to-segment to annotate one fruit, duplicate it for the rest, and export annotations in YOLO format for the client's training pipeline.
Use Cases
- Annotate thousands of images offline with AI assistance for custom YOLO training.
- Create oriented bounding box datasets for YOLOv8-OBB training using the toolkit.
- Prepare private medical or satellite imagery datasets without cloud upload.
- Batch convert and export annotations to YOLO, VOC, or YOLO-OBB formats.
- Use text prompts to auto-annotate objects like 'person, shoe, bus' with YOLO-World.
Models Under the Hood
as of 2026-08-18
Limitations
- The free open-source version requires Python and manual installation; the paid app is needed for one-click setup and toolkit features.
- Dataset toolkit, advanced exports, and priority support are gated behind Pro/Team plans.
- Updates are limited to 1 year on Starter and Pro, while Lifetime and Team plans include free updates forever.
- The tool is offline-first with no mention of an API or cloud collaboration.
as of 2026-08-18
Verification history
We have re-verified Labelme 4 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Labelme tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free Open Source
$0
Ideal for
Hobbyists or developers comfortable with Python who want to try annotation without paying.
What this tier adds
Free entry point: manual annotation shapes, no built-in AI models, requires Python installation.
Starter
$49 USD one-time
Ideal for
Solo practitioners who need AI-assisted annotation (SAM2/SAM3) with minimal setup.
What this tier adds
Adds one-click AI annotation (SAM2) and text-prompt (SAM3) with standalone app, no Python needed.
Pro
$79 USD one-time
Ideal for
Engineers and researchers building ready-to-train datasets for YOLO/VOC training.
What this tier adds
Adds dataset toolkit (10+ tools), ready-to-train exports, priority support (48h), and step-by-step guides.
Pro (Lifetime)
$249 USD one-time
Ideal for
Long-term users who want future updates and features for a one-time cost.
What this tier adds
Adds lifetime free upgrades including all future major versions; pays for itself in ~3 years vs annual Pro.
Team
$1,249 USD one-time
Ideal for
Small teams of up to 5 members needing shared access and lifetime updates.
What this tier adds
Adds 5 named seats (reassignable) and lifetime upgrades for the whole team; one invoice.
Where the pricing makes sense
The company stage and team size where Labelme's pricing actually pencils out — and where peers do it cheaper.
The one-time pricing fits solo practitioners and small teams who want to avoid recurring fees; cheaper than subscription tools like Roboflow over 2+ years, but cloud tools offer collaboration and APIs.
Setup time & first value
How long it actually takes to get something useful out of Labelme — broken out by persona, not the marketing-page minute.
Starter/Pro app: download and double-click, first annotation in under 5 minutes. Toolkit: install via pip, additional ~10 minutes to learn CLI. Open-source: Python setup takes 15-30 minutes, then similar.
Switching to or from Labelme
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From wkentaro/labelme (open source): paid app offers one-click install, built-in AI models, and toolkit — no need to configure Python or weigths manually.
- →From LabelMe previous versions: v7.0 updates include dark mode, settings dialog, and past-edge annotation; your existing annotation files remain compatible.
- ↗To Roboflow or Supervisely: export annotations to YOLO or VOC format, then import into the cloud platform for collaboration and API access.
Resources & Guides
Tutorials & Learning
A quick but comprehensive guide to LabelMe - an image/video annotation tool for deep learning
OVision
How to Install and Use LabelMe for Image Annotation | Step-by-Step Tutorial for Beginners
GIS & RS Made Easy
How to install and do Annotation of Images using Labelme (Easy, Simple & Flexible)
Goutam Borthakur
Official links
Tools that pair well with Labelme
Common stack mates teams adopt alongside Labelme, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Labelme vs The New Black
For fashion professionals accelerating design-to-prototype, The New Black is purpose-built with specialized AI features like tech pack export and virtual try-on. For computer vision teams needing private, offline annotation, Labelme offers unmatched control with on-device AI and no recurring fees. Choose based on your domain: fashion creation vs. dataset preparation — they serve fundamentally different needs.
Labelme vs Adobe Firefly Services
Choose Adobe Firefly Services if you need generative image APIs at scale with enterprise compliance and Adobe ecosystem integration. Choose Labelme if you require offline, AI-assisted annotation for computer vision datasets, especially for YOLO training, with data sovereignty.
Labelme vs Qoves
If you want a science-backed, non-surgical glow-up plan based on your facial biometrics, QOVES is your choice—it's a one-time analysis with detailed recommendations. If you need a private, offline image annotation tool for computer vision datasets with AI assistance, Labelme is the clear winner—it offers a free open-source version plus affordable one-time paid options. These tools serve entirely different purposes, so your decision depends on whether you're improving your face or labeling images.
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Frequently Asked Questions
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