đ Marqo-FashionSigLIP Model Card
Marqo-FashionSigLIP utilizes Generalised Contrastive Learning (GCL). This enables the model to be trained on not only text descriptions but also categories, styles, colors, materials, keywords, and fine - details. As a result, it can provide highly relevant search results for fashion products. The model is fine - tuned from ViT - B - 16 - SigLIP (webli).
Github Page: Marqo-FashionCLIP
Blog: Marqo Blog
đ Quick Start
The model can be used with OpenCLIP effortlessly.
đģ Usage Examples
Basic Usage
import open_clip
model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionSigLIP')
tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionSigLIP')
import torch
from PIL import Image
image = preprocess_val(Image.open("docs/fashion-hippo.png")).unsqueeze(0)
text = tokenizer(["a hat", "a t-shirt", "shoes"])
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print("Label probs:", text_probs)
đ Documentation
Benchmark Results
The following are the average evaluation results on 6 public multimodal fashion datasets (Atlas, DeepFashion (In - shop), DeepFashion (Multimodal), Fashion200k, KAGL, and Polyvore):
Text - To - Image (Averaged across 6 datasets)
Model |
AvgRecall |
Recall@1 |
Recall@10 |
MRR |
Marqo - FashionSigLIP |
0.231 |
0.121 |
0.340 |
0.239 |
FashionCLIP2.0 |
0.163 |
0.077 |
0.249 |
0.165 |
OpenFashionCLIP |
0.132 |
0.060 |
0.204 |
0.135 |
ViT - B - 16 - laion2b_s34b_b88k |
0.174 |
0.088 |
0.261 |
0.180 |
ViT - B - 16 - SigLIP - webli |
0.212 |
0.111 |
0.314 |
0.214 |
Category - To - Product (Averaged across 5 datasets)
Model |
AvgP |
P@1 |
P@10 |
MRR |
Marqo - FashionSigLIP |
0.737 |
0.758 |
0.716 |
0.812 |
FashionCLIP2.0 |
0.684 |
0.681 |
0.686 |
0.741 |
OpenFashionCLIP |
0.646 |
0.653 |
0.639 |
0.720 |
ViT - B - 16 - laion2b_s34b_b88k |
0.662 |
0.673 |
0.652 |
0.743 |
ViT - B - 16 - SigLIP - webli |
0.688 |
0.690 |
0.685 |
0.751 |
Sub - Category - To - Product (Averaged across 4 datasets)
Model |
AvgP |
P@1 |
P@10 |
MRR |
Marqo - FashionSigLIP |
0.725 |
0.767 |
0.683 |
0.811 |
FashionCLIP2.0 |
0.657 |
0.676 |
0.638 |
0.733 |
OpenFashionCLIP |
0.598 |
0.619 |
0.578 |
0.689 |
ViT - B - 16 - laion2b_s34b_b88k |
0.638 |
0.651 |
0.624 |
0.712 |
ViT - B - 16 - SigLIP - webli |
0.643 |
0.643 |
0.643 |
0.726 |
đ License
This model is licensed under the apache - 2.0
license.
đ Information Table
Property |
Details |
Tags |
clip, e - commerce, fashion, multimodal retrieval, siglip |
Library Name |
open_clip |
Pipeline Tag |
zero - shot - image - classification |
License |
apache - 2.0 |
Datasets |
Marqo/atlas, Marqo/deepfashion - inshop, Marqo/deepfashion - multimodal, Marqo/fashion200k, Marqo/iMaterialist, Marqo/KAGL, Marqo/polyvore |
Language |
en |
Metrics |
precision, recall, MRR |