marqo fashionSigLIP EU AI Act Compliance Profile

Marqo-FashionSigLIP Model Card

Marqo Fashion Siglip 2 is available. marqo-fashion-SigLip-2 has shown a further 78% improvement in MMR and recall vs marqo-fashion-SigLip. Contact Marqo to learn more: https://www.marqo.ai/book-demo

Marqo-FashionSigLIP is a multimodal embedding model that provides up to 57% improvement in MRR and recall over fashion clip.

Marqo-FashionSigLIP leverages Generalised Contrastive Learning (GCL) which allows the model to be trained on not just text descriptions but also categories, style, colors, materials, keywords and fine-details to provide highly relevant search results on fashion products. The model was fine-tuned from ViT-B-16-SigLIP (webli).

Github Page: Marqo-FashionCLIP

Blog: Marqo Blog

Usage

Hugging Face

The model can be loaded with AutoModel by

from transformers import AutoModel, AutoProcessor
model = AutoModel.from_pretrained('Marqo/marqo-fashionSigLIP', trust_remote_code=True)
processor = AutoProcessor.from_pretrained('Marqo/marqo-fashionSigLIP', trust_remote_code=True)

import torch
from PIL import Image

image = [Image.open("docs/fashion-hippo.png")]
text = ["a hat", "a t-shirt", "shoes"]
processed = processor(text=text, images=image, padding='max_length', return_tensors="pt")

with torch.no_grad():
    image_features = model.get_image_features(processed['pixel_values'], normalize=True)
    text_features = model.get_text_features(processed['input_ids'], normalize=True)

    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

print("Label probs:",

Your obligations as a marqo fashionSigLIP deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Library