metadata
tags:
- pytorch
- image-quality-assessment
- super-resolution
- hallucination-assessment
- arxiv:2610.25770
datasets:
- dhryou/VHA-bench
VHA+
VHA+ is a lightweight hallucination assessor for ground-truth and super-resolved image pairs. It uses a shared DINOv3 ViT-B/16 encoder and predicts VHA scores from image-pair features. Higher scores indicate more hallucination relative to the reference.
Download and evaluate
This repository provides the original step-20,000 checkpoint as vhaplus.pt, without optimizer state. Use it with the accompanying GitHub implementation; it is a custom PyTorch checkpoint.
After installing the GitHub repository and preparing VHA-bench under data/benchmark/:
from huggingface_hub import hf_hub_download
checkpoint = hf_hub_download(
repo_id="dhryou/VHA-plus",
filename="vhaplus.pt",
local_dir="data/checkpoints",
)
python scripts/vha+/evaluate.py \
--checkpoint data/checkpoints/vhaplus.pt \
--output outputs/vhaplus_evaluation
Checkpoint details
- Shared DINOv3 ViT-B/16 encoder; the last four transformer blocks are trained.
- Final normalized CLS features and the checkpoint's stored reporting scale.
- Input resolution: 512 × 512.
- Training step: 20,000.
- The
config.jsonrecords the model configuration and reporting scale; the GitHub implementation loadsvhaplus.ptdirectly.
On VHA-bench, the released model achieves Spearman 0.659 and Pearson 0.675 with human hallucination judgments. These are the paper's fixed benchmark results, not a new evaluation performed during this upload.