技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
evaluation【数据与研究】Evaluate GroundingDINO on COCO-style data and diagnose zero-shot AP results.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill when a user wants COCO zero-shot evaluation, bbox AP summaries, or evaluator API guidance for GroundingDINO. It covers dataset layout, config/checkpoint/device choices, category-caption postprocessing, and common AP failure modes.
pycocotools import errors, CUDA/device mismatch, missing custom ops, or unexpectedly low AP, use troubleshooting.instances_val2017.json and an image directory containing the referenced file_name entries.cuda, cuda:0, or cpu; CPU is useful for smoke tests but not benchmark-speed evaluation.python sub-skills/evaluation/scripts/grounding_dino_coco_eval.py \
-c configs/GroundingDINO_SwinT_OGC.py \
-p weights/groundingdino_swint_ogc.pth \
--anno_path datasets/coco/annotations/instances_val2017.json \
--image_dir datasets/coco/val2017 \
--device cuda \
--num_select 300 \
--num_workers 4
Expected output includes the constructed category prompt, progress messages, the IoU metric: bbox COCO summary table, and Final results: [...] with the bbox stats list. With the matching Swin-T OGC config, Swin-T OGC checkpoint, and official COCO val2017 data, the documented benchmark signal is about 48.5 bbox AP; mini-subsets and CPU smoke tests are validation checks, not benchmark evidence.