LabelME
Web-based tool for labeling image datasets
- Status
- Not yet determined
- Last checked
- Unverified
- License
- Open source
- Pricing
- Free tier · from $0
What it is
LabelME is a web-based tool that allows users to annotate images with object labels and segmentation masks, facilitating the creation of labeled datasets for machine learning tasks.
LabelME stands out as a flexible, open-source tool specifically designed for creating labeled datasets with object labels and segmentation masks, making it ideal for research and educational projects.
When it fits — and when it doesn’t
A good fit for
Research teams needing a flexible tool to annotate image datasets with object labels and segmentation masks
Educational projects that require hands-on experience in creating labeled datasets
Look elsewhere if
Teams requiring real-time annotation capabilities or integration into existing cloud services
Projects that need high-throughput data labeling for large-scale production environments
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Limited documentation and support resources
Primary documentation is limited to basic usage examples; extensive troubleshooting information is scarce.
- 02
Performance issues with large datasets
Web-based interface can become unresponsive or slow when handling a high volume of images or annotations.
- 03
Limited integration options beyond Python ecosystem
Primary support and development focus is on Python, with limited official SDKs for other languages.
What it does well
Web-based interface for image annotation
Supports object labels and segmentation masks
Flexible data export formats
Pricing
- Free tier
- YesOpen source — free to use
- Paid from
- $0
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up LabelME
Setup steps, common gotchas and what to check after 30 days.