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name: prediction-evaluation description: "Use Pytorch-UNet prediction, mask conversion, evaluation, and Dice metric workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

prediction-evaluation

Use this sub-skill when the task is about running or adapting Pytorch-UNet inference, saving predicted mask images, visualizing predictions, computing validation Dice, or debugging prediction/evaluation shape and palette issues.

Route here for

• Prediction CLI usage built around the repository's predict.py interface: --model, --input, --output, --viz, --no-save, --mask-threshold, --scale, --bilinear, and --classes.
• API prediction with predict_img(net, full_img, device, scale_factor=1, out_threshold=0.5).
• Converting class-index masks to PIL images with mask_to_image(mask, mask_values).
• Handling checkpoint mask_values metadata during prediction output conversion.
• Single-image and batch-image prediction workflows, output filename rules, visualization-only runs, and no-save behavior.
• Evaluation with evaluate(net, dataloader, device, amp) and Dice helpers: dice_coeff, multiclass_dice_coeff, and dice_loss.
• Binary versus multiclass mask conventions, including thresholding, argmax, background handling, and class-index validation.

Do not use this sub-skill for

• Constructing or modifying the U-Net architecture, selecting encoder/decoder blocks, or explaining checkpoint parameter shapes; route to sibling model-api.
• Dataset directory setup, Carvana download, mask scanning, training split, optimizer/loss training loops, Weights & Biases setup, or data acquisition; route to sibling data-training when those files are present.
• Executing Kaggle data downloads, pretrained weight downloads, long training, or network-dependent examples. Kaggle download is credentialed/network-bound and is reference-only.

Read next

• references/api-reference.md for the callable prediction, mask conversion, evaluation, and Dice metric contracts.
• references/cli-reference.md for prediction CLI flags, command patterns, output filename behavior, and no-save/visualization rules.
• references/workflows.md for end-to-end single image, batch image, API prediction, evaluation, and metric recipes.
• references/troubleshooting.md for checkpoint, class-count, scale, output naming, visualization, palette, dataloader, Dice, CUDA, and AMP failure modes.

Safe bundled check

Run scripts/prediction_smoke.py from an environment where the Pytorch-UNet imports resolve. The script creates a tiny synthetic image and temporary dummy UNet checkpoint with mask_values, loads it, calls predict_img and mask_to_image, validates output size and class IDs, optionally saves a mask, and prints JSON. It performs no downloads and uses CPU by default.

Example smoke commands from this sub-skill directory:

python scripts/prediction_smoke.py
python scripts/prediction_smoke.py --save-mask smoke_mask.png

Use scripts/prediction_cli_wrapper.py as a dry-run-first wrapper for a user-provided Pytorch-UNet checkout. Use it to preview the underlying predict.py command and only pass --execute after the user approves reading checkpoints/images and writing masks.

Key decisions before prediction or evaluation

1. Match the checkpoint: --classes, --bilinear, and model construction must match the saved weights. Keep the popped mask_values for output conversion.
2. Choose binary versus multiclass handling: n_classes == 1 uses sigmoid plus --mask-threshold; n_classes > 1 uses channel argmax and ignores --mask-threshold.
3. Validate image channels: the prediction CLI constructs an RGB-style UNet(n_channels=3, ...); use the API route and a matching checkpoint for grayscale or non-RGB inputs.
4. Validate output mapping: mask_to_image maps predicted class IDs through mask_values; mismatched or too-short palettes silently leave unmapped IDs as zero.
5. For evaluation, use a sized dataloader that yields {"image": tensor, "mask": tensor} with label indices already mapped into the expected class range.