Annotation Workflow in FiftyOne: Pool to Trained Detector

A FiftyOne walkthrough demonstrates how to convert a 1,754-image unlabeled drone-inspection subset of the InsPLAD power-line dataset into a targeted training set for detecting tower ID plates, polymer insulators, glass insulators, and yokes. It uses CLIP embeddings to identify near-duplicates, visualize visual clusters with UMAP, test text and example-based similarity searches, and measure uniqueness and representativeness, then adds C-RADIO embeddings to capture complementary structure and combines all four prioritization signals while excluding already identified candidates. Images selected through this triage are manually annotated in the FiftyOne App, used to fine-tune an RF-DETR detector, and iteratively reviewed on unseen samples to correct weaknesses. Evaluation on a 100-image held-out set found strong performance for tower ID plates but substantially weaker recall for yokes, reflecting their less distinctive visual characteristics and more difficult search behavior. The workflow emphasizes that annotation budgets should be guided by data diversity, model signals, and class-specific search performance rather than random sampling, while noting that FiftyOne Enterprise’s Agentic Labeling can provide prompt-driven preliminary labels for human review.

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