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Bout Analytics Tab

Use Bout Analytics to turn YOLO detection or pose outputs into event-level summaries, ROI metrics, and review-ready exports.

At a glance

Best for Typical output Usually next
Bout timing, ROI occupancy, dwell time, reviewed exports Detailed bouts CSV, summary CSV, spreadsheet exports, run manifest Review, batch exports, or Tab 7 for pose workflows

Compatibility

Input type Supported in Tab 6 Notes
Detection-only labels Yes Uses box and center evidence for ROI logic
Pose labels Yes Can also use visible keypoints for richer ROI and object logic

Main workflow

Select source video
  -> Select YOLO output folder
  -> Draw ROIs if needed
  -> Set bout and ROI parameters
  -> Process & Analyze Bouts
  -> Review outputs

1. ROI management

Use this section to:

  • draw new ROIs
  • rename or delete ROIs
  • manage object or stimulus ROIs separately from arena zones

2. Analysis parameters

Typical inputs and settings:

Setting What it controls
Source Video Original video used for inference
YOLO Output Folder Folder containing the YOLO label outputs
Dataset YAML Optional metadata helper
Max Frame Gap How detections are stitched into bouts
Min Bout Duration Minimum event length
Video FPS Time conversion for reporting
ROI Entry / Exit Thresholds Evidence needed to enter or leave a zone
Keypoint-based ROI mode Uses a selected body point instead of only bbox evidence

3. Run and review

After Process & Analyze Bouts, you can:

  • inspect the bout table
  • filter results by ROI
  • open the quick review tool
  • open the advanced bout scorer for frame-accurate edits

4. Outputs

A completed run can write:

  • detailed bouts CSV
  • summary CSV
  • spreadsheet-ready exports
  • optional ROI and object interaction files
  • run_manifest.json

That manifest is what later handoffs use.

5. Handing off to Tab 7

Tab 6 includes a direct button to continue into Tab 7 with the latest run.

That handoff is most useful for pose workflows because:

  • Tab 7 can reuse bout segmentation and metadata from the manifest
  • Tab 7 still recomputes modeling features from pose data

Detection-only runs still benefit fully from Tab 6, but they are not the main path into Tab 7.

Practical tips

  • Turn on tracking for multi-animal recordings whenever stable IDs matter.
  • Use keypoint-based ROI mode when a specific body part crossing a boundary is biologically important.
  • Keep batch outputs in clean per-video folders so the generated manifests remain easy to reuse later.