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name: evaluation description: "Evaluate pytorch-cifar100 checkpoints safely with test.py, metric interpretation, and checkpoint/path validation." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

evaluation

Use this sub-skill when a user already has a pytorch-cifar100 checkpoint and needs to run the repository evaluator, select safe evaluation options, or interpret the printed metrics. This sub-skill does not create checkpoints and does not catalog architectures beyond the net-name validation needed for evaluation.

Route

• In scope: building a safe test.py command, validating -net and -weights, choosing CPU/GPU and batch size, explaining CIFAR-100 test-data side effects, interpreting Top-1/Top-5 error and parameter-count output.
• Route to training: creating checkpoints, resuming training, changing optimization settings, or deciding which epochs to save.
• Route to model-zoo: model internals, architecture trade-offs, output-shape adaptation, or choosing among architecture families.

Concise workflow

1. Confirm the checkpoint is user-supplied; no pretrained checkpoints are bundled with this skill.
2. Pick the same CLI net name that produced the checkpoint. If uncertain, inspect the checkpoint path/name and use scripts/build_eval_command.py --list-nets for supported evaluator names.
3. Build but do not execute the command with scripts/build_eval_command.py; it checks net spelling, validates the weights path unless explicitly allowed, and warns about CIFAR-100 download and GPU behavior.
4. Run the printed command from the repository root so imports resolve and the evaluator can use ./data for CIFAR-100.
5. Read the end of stdout for Top 1 err, Top 5 err, and Parameter numbers; lower error is better, and the printed errors are fractions unless you convert them to percentages.

Bundled references

• references/evaluation-workflows.md — evaluator command, runtime behavior, metric interpretation, and CPU/GPU/batch choices.
• references/checkpoints.md — checkpoint naming, expected state_dict format, architecture matching, and safe checkpoint path selection.
• references/troubleshooting.md — common failures and targeted recovery steps.