ZooClaw-FashionSigLIP2 applies distilled full fine-tuning plus WiseFT interpolation to SigLIP2-base and reports outperforming LoRA, larger backbones, and external data on fashion retrieval benchmarks while releasing a new benchmark and bias analysis.
LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval
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abstract
In this paper, we present LookBench (We use the term "look" to reflect retrieval that mirrors how people shop -- finding the exact item, a close substitute, or a visually consistent alternative.), a live, holistic and challenging benchmark for fashion image retrieval in real e-commerce settings. LookBench includes both recent product images sourced from live websites and AI-generated fashion images, reflecting contemporary trends and use cases. Each test sample is time-stamped and we intend to update the benchmark periodically, enabling contamination-aware evaluation aligned with declared training cutoffs. Grounded in our fine-grained attribute taxonomy, LookBench covers single-item and outfit-level retrieval across. Our experiments reveal that LookBench poses a significant challenge on strong baselines, with many models achieving below $60\%$ Recall@1. Our proprietary model achieves the best performance on LookBench, and we release an open-source counterpart that ranks second, with both models attaining state-of-the-art results on legacy Fashion200K evaluations. LookBench is designed to be updated semi-annually with new test samples and progressively harder task variants, providing a durable measure of progress. We publicly release our leaderboard, dataset, evaluation code, and trained models.
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ZooClaw-FashionSigLIP2: Distilled Fine-tuning for Robust Fashion Retrieval
ZooClaw-FashionSigLIP2 applies distilled full fine-tuning plus WiseFT interpolation to SigLIP2-base and reports outperforming LoRA, larger backbones, and external data on fashion retrieval benchmarks while releasing a new benchmark and bias analysis.