L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Productivité · SKILL.md

benchmarking

benchmarking

Validate and interpret CustomConcept101 benchmark artifacts for CLIP text alignment, CLIP image alignment, and DINO image alignment.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: benchmarking description: "Validate and interpret CustomConcept101 benchmark artifacts for CLIP text alignment, CLIP image alignment, and DINO image alignment." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

CustomConcept101 benchmarking

Use this sub-skill when you need to validate generated sample layouts or interpret CustomConcept101 evaluation results.

It covers:

  • sample_root/samples/*.png layout checks
  • sample_root/prompts.json prompt-stem coverage
  • +-separated target_paths parsing
  • CLIP text alignment, CLIP image alignment, and DINO image alignment outputs

It does not cover image generation or retraining. Route those tasks to ../inference/SKILL.md and ../training/SKILL.md.

Start here

  1. Read references/data-formats.md.
  2. Run scripts/validate_benchmark_layout.py before any expensive metric run.
  3. Read references/workflows.md for the evaluation contract.
  4. Check references/troubleshooting.md when PNG counts, prompt stems, CUDA, CLIP/DINO downloads, or pickle updates fail.

Constraints

  • The source evaluator is CUDA-only and may download CLIP/DINO weights.
  • The validator is layout-only and does not load models.
  • Benchmark target images stay external to the runtime tree.
Skills enregistrées