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YOLO-pose Workflow#

The YOLO-pose path is one validated BehaviorScope-X pose workflow. It uses one YOLO-pose checkpoint for detection, keypoint estimation, and frozen visual-feature extraction, then trains a temporal classifier on pose-derived and visual streams.

Inputs#

You need:

  • full-video annotation export from the GUI,
  • a class-name file,
  • a YOLO-pose .pt checkpoint,
  • a destination folder for sequence caches and training outputs.

1. Full-Video Cache#

Open YOLO-pose > Full-Video Cache.

This stage reads full videos and .annot files, runs YOLO-pose, creates crops and pose-derived features, and writes sliding-window NPZ files plus a sequence_manifest.json.

Recommended settings:

  • use the same keypoint layout used to train the YOLO-pose checkpoint,
  • keep manifest validation enabled,
  • use skip existing when resuming an interrupted cache build,
  • keep the output root specific to the dataset and split.

2. Train/Val Feature Cache#

Open YOLO-pose > Train/Val Feature Cache.

This stage precomputes frozen visual descriptors from the YOLO-pose backbone. Precomputing features makes classifier training faster and easier to reproduce because the temporal model sees a fixed feature cache.

3. Train + Validation Eval#

Open YOLO-pose > Train + Validation Eval.

This stage trains the temporal classifier. The classifier can use:

  • pose geometry streams,
  • visual descriptors from the YOLO-pose backbone,
  • class weighting,
  • temporal smoothing or threshold decoding,
  • optional model bundling.

When bundling is enabled, training writes a single .pt file containing both the classifier and the YOLO-pose checkpoint reference needed for inference.

4. Inference#

Open YOLO-pose > Inference for one video or YOLO-pose > Batch for a folder.

Use bundled models when possible. They reduce path mistakes because users do not need to supply a separate classifier, config, and YOLO checkpoint.

5. Ethograms + Bouts#

Open YOLO-pose > Ethograms + Bouts to create ethogram timelines and bout-duration summaries from YOLO-pose temporal prediction CSVs. Use *.smoothed_frames.csv files for frame-exact timelines; window prediction CSVs are converted by probability averaging or label voting.

Outputs#

Typical outputs include:

  • sequence NPZ cache,
  • feature cache,
  • classifier checkpoint,
  • config.json,
  • training log,
  • confusion matrices,
  • behavior CSV,
  • ethogram and bout summary CSVs,
  • optional keypoint CSV,
  • optional annotated review MP4.

Use YOLO-pose > Outputs to inventory these artifacts.

When To Use Legacy Clip Cache#

Use YOLO-pose > Legacy Clip Cache only when your dataset is already organized as class-folder clips. For new full-video behavioral work, use full-video annotations and full-video caches.