alternatif untuk
Hosted platform for labeling data and evaluating models, with managed labeling services. Produk tertutup dari Labelbox.
| Alat | Kecocokan | Bintang | Lisensi | Ketentuan | Self-hosted | Bahasa | Terbaru | Push terakhir |
|---|---|---|---|---|---|---|---|---|
| Label Studio | Pengganti penuh | 28k | Apache-2.0 | Open source | Ya | TypeScript | 1.23.2 | 2026-10-06 |
| CVAT | Sebagian | 17k | MIT | Open source | Ya | Python | v2.77.0 | 2026-10-06 |
| Labelme | Sebagian | 16k | GPL-3.0 | Open source | Ya | Python | v7.8.0 | 2026-10-05 |
| doccano | Sebagian | 11k | MIT | Open source | Ya | Python | v1.8.5 | 2026-04-14 |
| X-AnyLabeling | Sebagian | 11k | GPL-3.0 | Open source | Ya | Python | v4.1.0 | 2026-10-06 |
| Argilla | Sebagian | 5.1k | Apache-2.0 | Open source | Ya | Python | v2.8.0 | 2026-10-06 |
6 alternatif
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Labeling for images, text, audio, video and time series, with model-assisted pre-labeling.
CVAT is a leading data annotation platform for image, video, audio, and 3D datasets. It offers open-source, cloud, and enterprise products, as well as labeling services, with AI-assisted labeling, quality assurance, team collaboration, analytics, and developer APIs.
Image and video annotation for computer vision, with automatic annotation models.
Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.
Desktop polygon, box and point annotation for images.
Open source annotation tool for machine learning practitioners.
Text annotation for classification, sequence labeling and sequence-to-sequence tasks.
X-AnyLabeling: A lightweight, efficient, and unified cross-platform desktop application for annotating text, image, video, and multimodal data, combining versatile built-in tools with state-of-the-art AI models and flexible multi-format export.
Desktop image and video annotation with model-assisted labeling.
Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
Collects human feedback and annotations on text and LLM outputs.