marqo-fashionSigLIP

by Marqo

Fashion-domain visual embedding model fine-tuned with Generalised Contrastive Learning

1.4Mdl/month

77likes

203Mparams

HuggingFace Use in Pipeline

Identifiers

Model ID

Marqo/marqo-fashionSigLIP

Feature URI

mixpeek://image_extractor@v1/marqo_fashionsiglip_v1

Overview

Marqo FashionSigLIP is a ViT-B/16-SigLIP model fine-tuned on over 1M fashion products using Generalised Contrastive Learning (GCL). Unlike generic CLIP models, it trains on rich fashion metadata including categories, styles, colors, materials, and fine-grained product details, delivering up to 57% improvement in MRR and recall over previous fashion-specific models.

On Mixpeek, FashionSigLIP powers domain-specific visual search for e-commerce and retail, where generic embeddings miss style nuances like fabric texture, color palette, and silhouette that are critical for product discovery and recommendation.

Architecture

ViT-B/16-SigLIP (webli) backbone fine-tuned with Generalised Contrastive Learning on fashion-specific metadata (categories, styles, colors, materials, keywords). Sigmoid contrastive loss for efficient pairwise training. 768-dimensional shared image-text embedding space.

Mixpeek SDK Integration

from mixpeek import Mixpeek
mx = Mixpeek(api_key="YOUR_KEY")

mx.ingest(
    collection_id="fashion-catalog",
    source="s3://product-images/",
    extractors=[{
        "type": "visual_embedding",
        "model": "Marqo/marqo-fashionSigLIP",
        "output_feature": "fashion_embedding"
    }]
)

Capabilities

Fashion-optimized 768-dim visual embeddings
Text-to-image and image-to-image product search
Fine-grained attribute awareness (color, material, style, silhouette)
57% MRR improvement over FashionCLIP 2.0

Use Cases on Mixpeek

Visual product search for fashion e-commerce (find similar garments by style)

Automated product categorization and attribute tagging for catalogs

Recommendation engines that understand fashion-specific visual similarity

Benchmarks

Dataset	Metric	Score	Source
Fashion Product Retrieval	MRR improvement vs FashionCLIP 2.0	+57%	Marqo, 2024 — marqo-FashionCLIP GitHub
Fashion Category Classification	Recall improvement vs FashionCLIP 2.0	+57%	Marqo, 2024 — marqo-FashionCLIP GitHub