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name: evaluation description: "Score EdgeConnect outputs with pixel metrics and validate evaluation inputs for FID-style runs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Evaluation

Use this sub-skill when the task is about scoring EdgeConnect outputs after inference or checking whether evaluation inputs are paired correctly. It owns the pixel-metric workflow and the preflight checks that make PSNR/SSIM/MAE/FID comparisons trustworthy.

Covers

• paired ground-truth vs prediction image evaluation
• grayscale pixel metrics on saved outputs
• metric artifact generation such as metrics.npz
• FID input preflight for image directories or cached statistics files
• validation of output naming, count alignment, and missing-file problems

Read first

• references/evaluation-workflows.md for command shape and directory expectations.
• references/metrics-reference.md for how to interpret PSNR, SSIM, MAE, and FID in this repo.
• references/troubleshooting.md when the score command fails or the pairings look wrong.

Bundled helpers

• scripts/compute_pixel_metrics.py computes PSNR/SSIM/MAE for paired image directories and saves metrics.npz.
• scripts/check_eval_inputs.py validates paired pixel-metric inputs and FID-ready directories or cached mu/sigma statistics.

Use for

• comparing generated images against ground truth
• checking that result filenames line up with the expected source names
• verifying that a prediction directory is ready for pixel metrics
• validating FID input directories or precomputed statistic files before you launch a heavier run

Do not use for

• training or checkpoint creation
• building flists or fixing mask/edge layout problems
• checkpoint-backed inference command generation

FID note

The source repository's FID helper depends on pretrained Inception weights and network/cache behavior. This sub-skill therefore treats FID as a documented and validated workflow rather than a fully self-contained no-network scorer. Use the bundled input checker first, then run a compatible FID implementation in an environment where the weights are already cached or where downloads are explicitly acceptable.