tao-train-mask2former

To train Mask2Former models for universal image segmentation, you can use this Skill to automate training on custom datasets using configurable parameters and PyTorch dependencies. It supports panoptic, instance, and semantic segmentation modes.

The best way to export and quantize Mask2Former models is using this Skill, which automates model export for deployment and applies quantization to achieve lower precision for better inference performance.

Yes, you need Docker and the NVIDIA Container Toolkit to run Mask2Former training and inference. The Skill also depends on PyTorch, torchvision, torchtext, pandas, numpy, and opencv-python libraries.

Yes, you can evaluate trained Mask2Former models against predefined datasets and metrics. The Skill automates the evaluation process to measure model performance before export and deployment.

Mask2Former inference works by running trained models on new images to generate segmentations. This Skill automates the inference process, producing panoptic, instance, or semantic segmentation outputs based on your model configuration.

When using PyTorch for Mask2Former training, consider that execution requires Docker and NVIDIA Container Toolkit. Additionally, you need dependencies like torchvision, torchtext, pandas, numpy, and opencv-python properly configured in your environment.