5 posts
A short warning. LS's "Cloud Storage → Source Storage" feature looks like exactly what you want for local data. Use it and you get tens of thousands of phantom tasks that collide with the ones you actually imported.
A uniform "every Nth frame" sampler wastes labeler time on near-identical frames the model already nailed. Four signals do better — segment boundaries, per-segment uniform, low confidence, bbox jumps.
When you harvest Label Studio exports to fine-tune the next model, treating *everything* in the export as ground truth is how you train the model on its own predictions. The filters worth applying before a single byte goes into a training set.
A deep dive on the multi-project pattern for video pre-annotation — what forces the split, how one episode fans out, and when not to fight Label Studio's data model.
A Phase-1 pipeline for embodied-robot video data — MediaPipe + YOLO inference, action segmentation, Label Studio import — plus the boring path-abstraction decision that kept it from collapsing.