tao-train-oneformer

To train a OneFormer model for image segmentation, you can automate the training process by specifying parameters and datasets. This Skill handles the training workflow, requiring Docker, NVIDIA Container Toolkit, and PyTorch to process your image data.

Running OneFormer model inference and export requires Docker, NVIDIA Container Toolkit, PyTorch, and specific Python libraries. These dependencies provide the containerized environment needed to execute model inference and generate deployment-ready export files.

You can evaluate OneFormer model performance using predefined metrics and datasets. The evaluation process automates performance checks on your image segmentation models to ensure they meet your task requirements before deployment.

Universal image segmentation handles semantic, instance, and panoptic tasks within a single model architecture. OneFormer models process image data through PyTorch to unify these segmentation workflows, simplifying deployment.

NVIDIA Container Toolkit is required to deploy OneFormer models. It works with Docker to provide the GPU-accelerated containerized environment necessary for executing PyTorch-based image segmentation training and inference.