UPDATE: TATOR
New: the UI now bundles labeling, CLIP training, and model management in-browser, plus fresh labeling modes like Auto Class Corrector, one-click point-to-box, a...
New: the UI now bundles labeling, CLIP training, and model management in-browser, plus fresh labeling modes like Auto Class Corrector, one-click point-to-box, and multi-point prompts. Tator also introduces early SAM3 support (sam3_local/sam3_lite) with recipe mining and training marked WIP, while dataset management remains rough. This moves beyond simple suggestions/refinement toward more automated, point-driven box creation and stricter auto-class correction.
Point-to-box and auto class correction can boost throughput and reduce annotator effort.
SAM3 may improve quality, but WIP status implies stability and performance risks.
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Benchmark Auto Class Corrector precision/latency and one-click point-to-box quality vs manual boxes on your classes.
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Profile SAM3 local vs lite resource usage and verify YOLO exports remain consistent under the new UI.
Legacy codebase integration strategies...
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Validate existing datasets and label schemas load/export unchanged with the bundled UI.
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Plan a fallback if SAM3 features degrade accuracy or speed in current pipelines.
Fresh architecture paradigms...
- 01.
Center labeling SOPs on one-click point-to-box plus auto class correction for speed.
- 02.
Choose sam3_local or sam3_lite based on hardware and desired annotation quality.