dataset_info:
features:
- name: image
dtype: image
- name: category1
dtype: string
- name: category2
dtype: string
- name: category3
dtype: float64
- name: text
dtype: string
- name: item_ID
dtype: string
splits:
- name: data
num_bytes: 143032788.688
num_examples: 42537
download_size: 152932414
dataset_size: 143032788.688
configs:
- config_name: default
data_files:
- split: data
path: data/data-*
Disclaimer: We do not own this dataset. DeepFashion dataset is a public dataset which can be accessed through its website.
This dataset was used to evaluate Marqo-FashionCLIP and Marqo-FashionSigLIP - see details below.
Marqo-FashionSigLIP Model Card
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
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-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)
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 |
|---|---|---|---|---|
| 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 |
When using the datset, cite the original work.
@inproceedings{liu2016deepfashion,
author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou},
title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations},
booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = June,
year = {2016}
}