Behavior classification#
Annolid supports two complementary behavior workflows:
Event scoring in the GUI (manual or semi-manual), with exports and summaries.
Model-based classification (optional / experimental), for projects that have labeled training data.
1) Event scoring and time budgets (recommended starting point)#
Record events in the GUI#
Open a video in Annolid.
Use your behavior labels (or launch with
annolid --flags "grooming,rearing,freezing").Mark events over time (start/end) and save.
Summarise behavior duration (“time budget”)#
After exporting events to CSV, compute summary statistics:
python -m annolid.behavior.time_budget /path/to/exported_events.csv -o time_budget.csv
To get a binned time course (e.g., 60-second bins):
python -m annolid.behavior.time_budget /path/to/exported_events.csv --bin-size 60 -o time_budget.csv
2) Train a behavior classifier (optional / experimental)#
Annolid includes behavior training utilities under annolid/behavior/.
The training entry point is:
python -m annolid.behavior.training.train --video_folder /path/to/labeled_clips
Note
The training dataset loader expects per-video CSV annotations (same stem as the video file) with columns such as Behavior and Trial time. If your data is in a different format, you’ll likely want to adapt annolid/behavior/data_loading/datasets.py to your project.