Good morning everyone! Hope everyone is keeping safe.
I want to thank everyone who contributed towards DLC- it’s a phenomenal tool!
I needed some help with some questions.
How do I decide which is the best snapshot-index for analyzing my videos? Do I try multiple evaluations for different snapshot index and choose the one with the lowest pixel error?
Do I follow the same aforementioned process when deciding which neural network to use? I decide the best model based on pixel error since I find it difficult to quantify which model is relatively better based on labeled images and hist.png plots since they look almost the same for different models.
Also, how do I go about benchmarking neural network on google COLAB? When I try calling deeplabcut.create_training_dataset(path_config_file, num_shuffles=3) to create different shuffles (where I will use each shuffle for different network) , it creates training dataset with different indices of dataset like here:
How do I create 3 copies of the exact same dataset for each shuffle, so that I can benchmark the neural network?
What to consider when tuning for optimal batch size in config.yaml and pose_cfg.yaml? Do I just increase it when the data is being processed slowly? Are there any downsides to increasing it? Should I also change learning rates since it affects batch processing?
Thank you so much for taking the time out to reply!