Streamlit
Labeled Frame Diagnostics
Analyze predictions of one or more networks on the train/test/val images.
Run the following command from inside the lightning-pose/lightning_pose/apps directory
(make sure you have activated your conda environment):
streamlit run labeled_frame_diagnostics.py -- --model_dir <ABOLUTE_PATH_TO_HYDRA_OUTPUTS_DIRECTORY>
The only argument needed is --model_dir, which tells the app where to find models and their predictions. <ABOLUTE_PATH_TO_HYDRA_OUTPUTS_DIRECTORY> should contain hydra subfolders of the type YYYY-MM-DD/HH-MM-SS.
The app shows:
plot of a selected metric (e.g. pixel errors, confidences) for each network and each body part, using bar/box/violin/etc plots.
scatterplot of a selected metric between two networks
Video Diagnostics
Visualizes multiple networks’ predictions on a test video.
From within lightning-pose/lightning_pose/apps, run:
streamlit run video_diagnostics.py -- --model_dir <ABOLUTE_PATH_TO_HYDRA_OUTPUTS_DIRECTORY>
where --model_dir is explained above.
The app shows:
timeseries of predictions/confidences/losses of a selected keypoint (x and y coordinate) for each network
boxplot/histogram of confidences/losses for each network and each body part