tao-train-pose-classification

Train ST-GCN models for pose classification by initiating a skill request with your dataset path and pre-trained checkpoint. This automates the training workflow, including hyperparameter tuning, to classify human poses in action sequences.

Evaluate pose classification models by running the evaluation command with your trained checkpoint and dataset path. This conducts automated performance benchmarking across multiple metrics to assess ST-GCN model accuracy.

Yes, you can automate pose recognition workflows using PyTorch. The skill utilizes deep learning libraries like torch and torchvision to support automatic model training and evaluation with pre-trained checkpoints.

Automated pose classification training handles hyperparameter tuning internally during the model training process. It uses your specified pre-trained checkpoint and dataset to optimize the ST-GCN model for skeletal action recognition.

Yes, you need PyTorch deep learning libraries including torch, torchvision, and torch.nn to run ST-GCN pose classification training and evaluation workflows for skeletal action recognition.