@mervenoyann：这条管线的第二天发现 > 它有效，在道路标志检测中针对人工标注得到了 map@50=0.8028，使用了……

day 2 findings on this pipeline > it works, got map@50=0.8028 on road sign detection against human annotations, with only 1.3k examples see results below > Liquid rejects way more than Gemma-4 (530 vs 306 in hard document parsing, 1022 vs 116 in easy road sign detection, tbh it's smaller and more prone to hallucination when I vibe check) > in some cases (see document media parsing examples below) trained RF-DETR outperforms Qwen annotations it was trained on which is super cool, sometimes judges introduce bboxes (and I don't remove them) it's a win? > multiple VLMs as judges will shrink your dataset depending on the difficulty of the problem, sometimes taking only one "correct" from a judge is enough. since you are training small models it's better to kickoff training for consensus and single correct verdict separately > use super-specific prompts of what you want and don't want in labelling and judging especially if your labels as words could mean many things next up: make this library leaner to generalize better to be problem-agnostic, try again on segmentation, actually use Gemma for orchestration