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name: evaluation-and-logging description: "Guide PSNR/SSIM result-pair evaluation and TensorBoard/W&B logging for the SR3/DDPM super-resolution repository." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Evaluation and logging

Use this sub-skill when a task is about evaluating generated super-resolution outputs, interpreting PSNR/SSIM numbers, or enabling/diagnosing TensorBoard and Weights & Biases logging for this repository.

Route by intent

• Evaluate completed SR outputs: read metrics-and-logging.md and use evaluate_result_pairs.py on a directory containing final *_hr.png and *_sr.png images.
• Explain metric semantics: read metrics-and-logging.md before comparing scores across runs.
• Find the right output files: read metrics-and-logging.md for how sr.py, infer.py, and sample.py name images.
• Set up experiment logging: read metrics-and-logging.md for TensorBoard, W&B flags, tables, checkpoint artifacts, and supported scripts.
• Debug missing pairs, shape errors, or W&B failures: read troubleshooting.md.

Operating boundaries

• Result-pair PSNR/SSIM requires ground-truth HR images; unconditional samples without matching HR files are not valid for this evaluator.
• The bundled evaluator is self-contained and does not import repository modules, so it can be used on copied result folders.
• Training, inference, checkpoint acquisition, and dataset preparation are owned by other sub-skills; this sub-skill only interprets and checks the outputs and logging behavior they produce.