nv-generate-mr-brain-finetune

To finetune an MR-brain diffusion UNet, this workflow validates your MONAI-style datalist, stages required environment configs, and delegates execution to upstream scripts to produce a finetuned checkpoint and workflow summary.

The staging process for MR-brain diffusion finetuning involves validating input datalists, generating required environment and config JSONs, and orchestrating the execution of upstream training scripts to ensure a repeatable workflow.

Yes, you can run inference after finetuning an MRI diffusion model. The workflow orchestrates optional infer steps alongside create_training_data and train scripts to generate optional inference outputs and artifacts.

Yes, you need PyTorch and MONAI along with nibabel and numpy dependencies to run the MR-brain finetuning wrapper. These libraries are required to stage the environment and execute the upstream diffusion training scripts.

The MR-brain diffusion UNet finetuning workflow generates a finetuned checkpoint, optional inference outputs, and a workflow summary. These artifacts provide traceability and usable model weights for downstream tasks.

Using a wrapper for MR-brain diffusion finetuning provides repeatable preflight validation and environment staging. It minimizes manual setup by orchestrating upstream scripts automatically, ensuring datalist paths are correct before training begins.