Users report that Labelbox excels in its Labeler Quality with a score of 9.3, while Supervisely's score of 9.0 indicates it may not be as robust in this area. Reviewers mention that Labelbox's intuitive interface significantly enhances the labeling process.
Reviewers mention that Supervisely shines in Machine Learning Pre-Labeling with a score of 9.3 compared to Labelbox's 8.6. Users on G2 appreciate the efficiency it brings to the data labeling workflow, allowing for quicker project turnaround times.
G2 users highlight that Labelbox's Data Quality score of 9.3 is impressive, but Supervisely outperforms with a score of 9.5. Users report that Supervisely's tools for Error and Outlier Identification are particularly effective in maintaining high data standards.
Users say that Labelbox's Task Quality is rated at 9.1, which is slightly lower than Supervisely's 9.3. Reviewers mention that Supervisely's features for Object Detection and Object Tracking are particularly well-received, enhancing the overall task performance.
Reviewers mention that Labelbox has a strong Human-in-the-Loop feature with a score of 8.9, but Supervisely's score of 8.6 indicates it may not be as effective in integrating human feedback into the model training process. Users report that Labelbox's approach fosters better collaboration between data scientists and annotators.
Users on G2 report that while both platforms have strong Model Performance Insights, Labelbox's overall user experience is rated at 9.0 for Ease of Use, compared to Supervisely's perfect score of 10.0. Reviewers mention that the simplicity of Supervisely's interface makes it easier for new users to get started quickly.
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Labelbox
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Supervisely Computer Vision Platform
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Free Trial
Labelbox
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Supervisely Computer Vision Platform
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Ratings
Meets Requirements
9.0
41
9.3
7
Ease of Use
9.0
41
10.0
7
Ease of Setup
9.0
21
Not enough data
Ease of Admin
9.0
5
Not enough data
Quality of Support
9.0
39
9.0
7
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