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YOLO (You Only Look Once)

YOLO (You Only Look Once) is a family of single-stage real-time object-detection models (YOLOv1 → YOLOv8 → YOLOv10+). Typical framing: a single neural-network pass produces bounding boxes + class labels + confidence scores over an entire image, in contrast to two-stage detectors (R-CNN family) that first propose regions then classify them. The single-stage design trades a small accuracy delta at the top end for order-of-magnitude inference speed, making YOLO the default choice for real-time + edge CV deployments.

Stub page — expand as more YOLO-internals sources land on the wiki.

Training notes (from ingested source)

  • Cosine learning-rate scheduling + AdamW optimisation on PyTorch 2.1 named as "critical for stabilizing the larger YOLOv8l model variant and preventing gradient divergence during training" (sources/2026-04-01-aws-automate-safety-monitoring-with-computer-vision-and-generative-ai).
  • YOLO annotation format (.txt per image with normalised bounding-box coords + class index) is the canonical training input; GLIGEN-generated scenes are converted to this format by parallel Python workers downstream of synthetic-data generation.
  • Used for simultaneous multi-dimensional detection — in the AWS safety-monitoring PPE module, one YOLO model locates workers + classifies presence/absence of required safety equipment + applies contextual analysis to determine which PPE items are mandatory for the specific zone.

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