Marqo-FashionSigLIP
| Property | Value |
|---|---|
| Parameter Count | 203M |
| License | Apache 2.0 |
| Tensor Type | F32 |
| Architecture | SigLIP-based Vision-Language Model |
What is marqo-fashionSigLIP?
Marqo-fashionSigLIP is an advanced multimodal embedding model specifically designed for fashion e-commerce applications. Built upon the ViT-B-16-SigLIP architecture and fine-tuned using Generalised Contrastive Learning (GCL), this model demonstrates exceptional performance in fashion product retrieval and classification tasks, offering up to 57% improvement in Mean Reciprocal Rank (MRR) and recall compared to previous fashion-specific CLIP models.
Implementation Details
The model leverages a sophisticated architecture that can process both visual and textual data, incorporating not just basic product descriptions but also detailed attributes like categories, styles, colors, and materials. It can be easily integrated using popular frameworks like Hugging Face Transformers, OpenCLIP, and even Transformers.js for browser-based applications.
- Built on ViT-B-16-SigLIP (webli) architecture
- Implements Generalised Contrastive Learning for enhanced multimodal understanding
- Supports multiple integration paths including Python and JavaScript
- Optimized for both text-to-image and category-to-product retrieval
Core Capabilities
- Achieves state-of-the-art performance in fashion product retrieval with 0.231 average recall
- Excels in category-to-product matching with 0.812 MRR
- Supports zero-shot image classification
- Handles fine-grained fashion attribute understanding
- Enables efficient multimodal search and retrieval
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its specialized training using GCL, allowing it to understand complex fashion attributes and relationships. It significantly outperforms existing fashion-specific models across multiple benchmark datasets, making it particularly valuable for e-commerce applications.
Q: What are the recommended use cases?
The model is ideal for fashion e-commerce platforms, particularly for implementing visual search, product recommendation systems, and automated product categorization. It excels in both text-to-image search and category-based product retrieval scenarios.
