This PEFT adapter was trained on a 40,000-row stratified subset, not the entire training split, of Visual Jev decisions v1 at revision 687c745c34846d104ee85af802b9fd444a854f5d. It answers a task-specific question about an image with a normalized box, choice, or short text. The GitHub composite shows selected held-out predictions with annotated photos. Its companion JSON retains full predictions. Published train and evaluation questions largely use five fixed templates. COCO instance annotations supply grounding, box choice, and left/right questions; Visual Genome v1.2 supplies selected color and relation labels. Objects, boxes, and targets vary by photo, and a photo yields only supported…
Independent publisher
Harsh Nandwana
harshnandwana
Models in Library2
Datasets in Library0
Models on Hugging Face2
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Published tasks: Image and text to text.
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Models
This PEFT adapter was trained on a 120,000-row stratified subset, not the entire training split, of Visual Jev decisions v1 at revision 687c745c34846d104ee85af802b9fd444a854f5d. It answers a task-specific question about an image with a normalized box, choice, or short text. The two published prediction logs contain the same 1,000 held-out test IDs. The 120k adapter improved all five selected-subset scores, including mean grounding IoU from 0.530 to 0.598, box choice accuracy from 94.0% to 97.5%, and spatial decision accuracy from 90.0% to 94.5%. This is not a full test-split benchmark. Modal's billing report measured $8.23 metered for the 120k staging, training, benchmark, and automatic…