DiffVQA models trained
end-to-end
(encoder + head jointly). Each
.pt
file
is a plain state dict of
DiffVQAHead
. MAE-ViT-L/16 is the primary encoder studied.
Results (test set, MAE-ViT-L/16)
BLEU-4
ROUGE-2
RadGraph-s
BERTScore F1
0.472
0.573
0.288
0.938
File
Encoder
vis_dim
clip-vit-l14_best.pt
CLIP ViT-L/14
1024
coca_best.pt
CoCa
768
florence2_best.pt
Florence-2
1024
mae-vit-l16_best.pt
MAE ViT-L/16
1024
siglip_best.pt
SigLIP
1152
Loading
import torch
from lapvqa.diffvqa.model import DiffVQAHead
ckpt = torch.load("mae-vit-l16_best.pt", map_location="cpu")
head = DiffVQAHead(vis_dim=1024)
head.load_state_dict(ckpt)
head.eval()
Runs of dmusingu lapvqa-diffvqa-native on huggingface.co
0
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
0
30-day runs
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