yanggangu/wudi-featcal-b32-ta8

WUDI-FeatCal CLIP ViT-B/32 TA8

This repository contains a merged CLIP vision encoder for the TA8 image-classification benchmark. The model is based on openai/clip-vit-base-patch32 and was produced with WUDI-Merging followed by FeatCal calibration. This model is released as part of our work:

• FeatCal: Feature Calibration for Post-Merging Models
• Paper: https://arxiv.org/abs/2605.13030

This is a transformers.CLIPVisionModel, not a full CLIPModel. Use the processor from openai/clip-vit-base-patch32.

FusionBench:

• GitHub: https://github.com/tanganke/fusion_bench
• Documentation: https://tanganke.github.io/fusion_bench/

Load

from transformers import CLIPProcessor, CLIPVisionModel

model = CLIPVisionModel.from_pretrained("yanggangu/wudi-featcal-b32-ta8")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

Evaluation

Evaluation was run with FusionBench using:

fusion_bench \
  method=dummy \
  modelpool=CLIPVisionModelPool/clip-vit-base-patch32_TA8 \
  modelpool.models._pretrained_=yanggangu/wudi-featcal-b32-ta8 \
  taskpool=CLIPVisionModelTaskPool/clip-vit-classification_TA8 \
  seed=42

SUN397	Cars	RESISC45	EuroSAT	SVHN	GTSRB	MNIST	DTD	Average
72.2	76.0	93.0	98.1	96.7	97.9	99.4	77.1	88.8

Raw accuracies:

sun397:        0.7221158742904663
stanford-cars: 0.7598557472229004
resisc45:      0.9301587343215942
eurosat:       0.9807407259941101
svhn:          0.9672326445579529
gtsrb:         0.9792557358741760
mnist:         0.9944999814033508
dtd:           0.7707446813583374
average:       0.8880755156278610

Comparison With FusionBench Reference Results

The table below compares this model with selected CLIP ViT-B/32 TA8 reference results reported in the FusionBench documentation.

This model is not the overall best number in the public FusionBench table because Weight-Ensembling MoE reports a higher average score, but it is competitive with the strongest entries while remaining a single dense CLIPVisionModel with the same parameter count as the base vision encoder.

Method	SUN397	Cars	RESISC45	EuroSAT	SVHN	GTSRB	MNIST	DTD	Average
Fine-tuned (STL)	75.0	78.3	95.2	99.0	97.3	98.9	99.6	79.7	90.3
Traditional MTL	72.3	76.6	92.2	97.9	95.5	97.7	99.3	77.7	88.6
RegMean++	69.3	70.5	86.7	96.1	94.1	90.4	99.0	68.7	84.4
WUDI-Merging	68.0	72.5	85.0	94.6	94.8	94.9	99.3	66.6	84.5
Weight-Ensembling MoE	73.7	76.8	93.4	98.2	96.8	98.2	99.6	76.6	89.2
WUDI-Merging + FeatCal (this model)	72.2	76.0	93.0	98.1	96.7	97.9	99.4	77.1	88.8

Reference: https://tanganke.github.io/fusion_bench/modelpool/clip_vit/

Citation

@article{gu2026featcal,
  title={FeatCal: Feature Calibration for Post-Merging Models},
  author={Gu, Yanggan and Cai, Shuo and Wang, Zihao and Wang, Wenjun and Wang, Yuanyi and Wang, Pengkai and Huang, Sirui and Lu, Su and Wu, Jianmin and Yang, Hongxia},
  journal={arXiv preprint arXiv:2605.13030},
  year={2026}
}