metadata

tags:
  - clip
  - e-commerce
  - fashion
  - multimodal retrieval
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

Marqo-FashionCLIP Model Card

Marqo-FashionCLIP 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 (laion2b_s34b_b88k).

Github Page: Marqo-FashionCLIP

Blog: Marqo Blog

Usage

The model can be seamlessly used with OpenCLIP by

import open_clip
model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionCLIP')
tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionCLIP')

Benchmark Results

Average evaluation results on 6 public multimodal fashion datasets (Atlas, DeepFashion (In-shop), DeepFashion (Multimodal), Fashion200k, KAGL, and Polyvore) are reported below:

Text-To-Image (Averaged across 6 datasets)

Model	AvgRecall	Recall@1	Recall@10	MRR
FashionCLIP2.0	0.163	0.077	0.249	0.165
Marqo-FashionCLIP	0.192	0.094	0.290	0.200
OpenFashionCLIP	0.132	0.060	0.204	0.135
ViT-B-16-laion2b_s34b_b88k	0.174	0.088	0.261	0.180

Category-To-Product (Averaged across 5 datasets)

Model	AvgP	P@1	P@10	MRR
FashionCLIP2.0	0.684	0.681	0.686	0.741
Marqo-FashionCLIP	0.705	0.734	0.676	0.776
OpenFashionCLIP	0.646	0.653	0.639	0.720
ViT-B-16-laion2b_s34b_b88k	0.662	0.673	0.652	0.743

Sub-Category-To-Product (Averaged across 4 datasets)

Model	AvgP	P@1	P@10	MRR
FashionCLIP2.0	0.657	0.676	0.638	0.733
Marqo-FashionCLIP	0.707	0.747	0.667	0.772
OpenFashionCLIP	0.598	0.619	0.578	0.689
ViT-B-16-laion2b_s34b_b88k	0.638	0.651	0.624	0.712