Labelme vs Roboflow Annotate
Both tools support polygon, bounding box, and segmentation annotation for computer vision datasets. The key difference is deployment: Roboflow Annotate is a cloud-based platform with integrated dataset management and model training, while Labelme is a desktop tool that keeps all data local.
| Feature | Labelme | Roboflow Annotate |
|---|
| License | GPL-3.0 (open source) | Proprietary |
| Data storage | Local only, no uploads | Cloud (Roboflow servers) |
| AI-assisted annotation | SAM2, SAM3, YOLO-World offline | Smart polygon, label assist (cloud) |
| Dataset export | YOLO, YOLO-OBB, Pascal VOC (Pro) | Multiple formats |
| Team annotation | Single user | Multi-user with queues |
| Cost | Free / $79 one-time (Pro) | Subscription |
Labelme is the better fit when images cannot be uploaded to a third-party cloud, when annotation is a solo or small-team task, or when avoiding recurring subscription cost is a constraint. Roboflow Annotate is the better fit when a team needs concurrent annotation with queue management, integrated training pipelines, and dataset version control in one cloud workspace.
Labelme vs Labelbox
Labelbox is an enterprise data labeling platform built for teams with large-scale annotation needs, model evaluation, and active learning pipelines. Labelme is a desktop tool for individual or small-team annotation without cloud infrastructure.
| Feature | Labelme | Labelbox |
|---|
| License | GPL-3.0 (open source) | Proprietary |
| Data storage | Local only | Cloud (Labelbox servers) |
| Team features | None | Label queues, review workflows, assignment |
| AI annotation | Offline SAM2/SAM3/YOLO-World | Cloud-based AI assist |
| Pricing | Free / $79 one-time | Enterprise pricing |
For enterprise teams annotating at scale with cross-functional review workflows, Labelbox is built for that coordination. For teams where data must stay on-premise, or for individuals building training datasets without an enterprise budget, Labelme covers the annotation task at a fraction of the cost.
Labelme vs V7 Labs
V7 Labs is a cloud annotation platform with auto-labeling, dataset versioning, and NLP annotation support. Labelme handles image and video annotation locally, with no dataset versioning or NLP features.
For pure image annotation where privacy and cost are the primary drivers, Labelme is the stronger choice. V7 Labs is worth the subscription when a team needs dataset versioning, NLP annotation, or the auto-labeling pipeline integrated with model training in a managed cloud environment.
