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Pepijn
c026aed8f8
Spatial VQA answers (bbox / keypoint) were trained as pixel-coordinate JSON, which fights PaliGemma's detection prior and leaks <loc>-token salad at inference. Convert them to PaliGemma's native <locNNNN> vocabulary instead so the LM head reuses that prior. Training side (text_processor_pi052.py): a target turn whose content parses as a bbox/keypoint answer is rewritten to <loc> text, using the camera frame's native (H, W) from the observation and the preceding image block. Non-spatial answers, subtask/memory targets and SmolVLA2 keep their JSON form — the dataset stays backbone-agnostic. Runtime side (smolvla2/inference/vqa.py): parse_vqa_answer detects <loc> answers (2 locs -> keypoint, 4 -> bbox), returning normalized [0,1] coords with a normalized flag; draw_vqa_overlay denormalizes against the chosen camera frame's pixel size. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>