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name: model-api description: "Use Pytorch-UNet model architecture and checkpoint APIs for binary or multiclass semantic segmentation." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

model-api

Use this sub-skill when the task is about constructing, loading, checking, or adapting the Pytorch-UNet model API itself.

Route here for

• Creating UNet(n_channels, n_classes, bilinear=False) for binary or multiclass semantic segmentation.
• Explaining the encoder/decoder blocks: DoubleConv, Down, Up, and OutConv.
• Choosing input channels, output classes, bilinear upsampling, or transposed-convolution upsampling.
• Loading raw checkpoint state_dict files, including checkpoints that carry a non-parameter mask_values entry.
• Using torch.hub unet_carvana weights with the supported pretrained scale choices and network-download caution.
• Running a safe CPU or optional CUDA forward-pass smoke check before training, prediction, or evaluation work.

Do not use this sub-skill for

• Dataset directory layout, mask scanning, transforms, train/validation splitting, or the training CLI; route to data-training.
• Prediction image I/O, mask image conversion workflows, CLI output naming, or evaluation dataloaders and Dice reporting; route to prediction-evaluation.
• Downloading Kaggle/Carvana data or executing credentialed/network data scripts.

Read next

• references/api-reference.md for the distilled model API, architecture blocks, tensor contracts, and checkpoint conventions.
• references/workflows.md for common construction, checkpoint loading, torch.hub, and smoke-check workflows.
• references/troubleshooting.md for channel/class mismatches, checkpoint loading failures, CUDA/AMP issues, and torch.hub download failures.

Bundled safe check

Run the bundled script scripts/model_smoke.py from an environment where the Pytorch-UNet package or checkout is importable. It imports UNet, creates a tiny tensor, validates the output shape, and prints JSON. CPU is sufficient for functional checks; CUDA and AMP are optional accelerators.