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# Learning Curve Configuration Example

# This file configures the learning curve analysis script

# Path to the DeepLabCut project config.yaml file

# IMPORTANT: Update this path to point to your actual config.yaml file

config_path: '/home/alek/projects/cdl-test1/data/cdl-projects/test1-haag-2025-05-21/config_full.yaml'

# Experiment identifier (used in results filename)

experiment_id: 'lc_baseline'

# Fraction of data to set aside as test set (0.0 to 1.0)

# The remaining data will be used for training

test_fraction: 0.2

# Number of learning curve steps

# Each step will use progressively more training data

# Example with 5 steps: 20%, 40%, 60%, 80%, 100% of available training data

n_steps: 5

# Random seed for reproducibility

seed: 42

# Whether to group frames by video when splitting

# If true, entire videos stay together in train or test sets

# If false, frames are split randomly regardless of video

group_by_video: false

# Number of training epochs per step

epochs: 200

# Step to start from (useful for resuming interrupted runs)

# Set to 0 to start from the beginning

# Set to N to skip the first N steps (0-indexed)

start_from_step: 0

# Training parameter overrides

# These will be applied to the pytorch_config.yaml for each training run

# Note: Skeletal-related parameters are NOT used in this script

train_overrides:

runner.key_metric: 'test.rmse'

runner.key_metric_asc: false