_reliable_pose_analysis - LizbethMG/DLC_PoseAnalysis GitHub Wiki

Analyzing videos with DeepLabcut for pose estimation involves several steps to ensure the reliability and accuracy of the output. When assessing the reliability of the output from DeepLabcut, you should consider various characteristics and variables. Here are key aspects to focus on:

  1. P-Cutoff Value

Definition: A threshold that determines whether a detected body part's coordinates are reliable or not. It represents the confidence level of the pose estimation for each body part. How to Use: Set a p-cutoff value (e.g., 0.95) to filter out low-confidence detections. Higher p-cutoff values mean you trust only high-confidence detections.

  1. Likelihood Scores

Definition: Numerical values indicating the model's confidence in the accuracy of each detected body part's position. How to Use: Analyze the distribution of likelihood scores across all body parts and frames. Consistently high likelihood scores indicate reliable tracking. Low scores may suggest occlusions, poor lighting, or that the model struggles with certain poses or angles.

  1. Training Dataset Quality

Definition: The diversity and representativeness of the video frames and annotations used to train the DeepLabcut model. How to Use: Ensure your training dataset includes a variety of poses, angles, lighting conditions, and backgrounds that closely match your analysis videos. A well-trained model on a diverse dataset leads to more reliable pose estimation.

  1. Training Loss

Definition: A metric that quantifies the difference between the predicted poses by the model and the actual, annotated poses in the training dataset. How to Use: Monitor the training and validation loss during model training. A low and stable training loss, along with a validation loss that closely follows the training loss, indicates a well-fitted model.

  1. Cross-Validation

Definition: A technique to assess how the model will generalize to an independent dataset. How to Use: Perform cross-validation by splitting your annotated dataset into training and testing subsets. A model that performs well on both training and testing data is considered more reliable.

  1. Visual Inspection (Super important!)

Definition: Manually checking the overlaid predicted poses on the video frames. How to Use: Visually inspect a subset of video frames to ensure that the model's predictions match the actual poses and movements of the subjects. This qualitative assessment can help identify issues not evident through quantitative metrics.

  1. Reproducibility

Definition: The consistency of model predictions across different runs or when analyzing similar videos. How to Use: Test the model on similar videos or the same video multiple times to ensure that the predictions are consistent and reproducible.