tao-train-metric-learning-recognition

Train metric learning models for fine-grained visual recognition by running the skill with your specified dataset. It manages the training pipeline and outputs model checkpoints alongside performance metrics.

Metric learning in machine learning workflows is used for fine-grained visual recognition tasks like retail product recognition. It trains models to differentiate highly similar visual classes by learning distance metrics.

Yes, Docker and nvidia-container-toolkit are required to run TAO metric learning recognition. These dependencies execute the Python libraries and manage the GPU environment for model training and inference.

Evaluate and export trained metric learning recognition models by using the evaluate and export actions. Evaluation provides detailed performance benchmark metrics, while export prepares the model for inference.

Yes, you can run inference on trained TAO metric learning models with various input formats. The inference action processes these inputs using the exported model to generate recognition predictions for your visual data.