ZooClaw-FashionSigLIP2: Distilled Fine-tuning for Robust Fashion Retrieval

Siqiao Xue, Chunxue Xu

ZooClaw-FashionSigLIP2: Distilled Fine-tuning for Robust Fashion Retrieval: 3 upvotes on Hugging Face Daily Papers, #40 of 49 papers on 2026-06-30. Day-by-day upvote history.

Adapting a foundation vision-language encoder to a specialized retrieval task creates a fundamental tradeoff: gains on the target distribution come at the cost of the foundation model's broad generalization, and fashion retrieval is a stringent instance of this problem. We present ZooClaw-FashionSigLIP2, a fashion-specialized SigLIP2-base model that resolves this tradeoff with a simple recipe -- full fine-tuning with knowledge distillation on curated in-domain data, followed by \wiseft~wortsman2022wiseft weight interpolation with the base model -- and outperforms LoRA, larger backbones (up to 1B parameters), and external training data. Under fair evaluation, ZooClaw-FashionSigLIP2 outperforms all baselines on every benchmark in our suite. In addition, we release ZooClaw-Fashion, a new high-quality fashion retrieval benchmark, and a systematic quality analysis of widely-used benchmarks that exposes and mitigates structural biases in their public ground truth. We open-source the model weights and all evaluation artifacts to facilitate future research.

Paper page on Hugging Face · arXiv

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