Giuseppe Cartella, Alberto Baldrati, Davide Morelli, Marcella Cornia, Marco Bertini, Rita Cucchiara

This is the official repository for the paper OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data, ICIAP 2023.

• 31 August 2023 Release of the inference code and checkpoint!
• 1 July 2023 Our work has been accepted for publication to ICIAP 2023 🎉 🎉 !!!

Abstract:
The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements. In the context of automatic tagging classification and multimodal retrieval, prior works either defined a low generalizable supervised learning approach or more reusable CLIP-based techniques while, however, training on closed source data. In this work, we propose OpenFashionCLIP, a vision-and-language contrastive learning method that only adopts open-source fashion data stemming from diverse domains, and characterized by varying degrees of specificity. Our approach is extensively validated across several tasks and benchmarks, and experimental results highlight a significant out-of-domain generalization capability and consistent improvements over state-of-the-art methods both in terms of accuracy and recall.

Clone the repository and create the ofclip conda environment using the environment.yml file:

conda env create -f environment.yml
conda activate ofclip

Download the weights of OpenFashionCLIP at this link.

OpenFashionCLIP can be used in just a few lines to compute the cosine similarity between a given image and a textual prompt:

import open_clip
from PIL import Image
import torch

device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Load the model
clip_model, _, preprocess = open_clip.create_model_and_transforms('ViT-B/32')
state_dict = torch.load('weights/openfashionclip.pt', map_location=device)
clip_model.load_state_dict(state_dict['CLIP'])
clip_model = clip_model.eval().requires_grad_(False).to(device)
tokenizer = open_clip.get_tokenizer('ViT-B-32')

#Load the image
img = Image.open('examples/maxi_dress.jpg')
img = preprocess(img).to(device)

prompt = "a photo of a"
text_inputs = ["blue cowl neck maxi-dress", "red t-shirt", "white shirt"]
text_inputs = [prompt + " " + t for t in text_inputs]

tokenized_prompt = tokenizer(text_inputs).to(device)

with torch.no_grad():
    image_features = clip_model.encode_image(img.unsqueeze(0)) #Input tensor should have shape (b,c,h,w)
    text_features = clip_model.encode_text(tokenized_prompt)
    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("Labels probs:", text_probs)

• Inference code and checkpoint.

If you make use of our work, please cite our paper:

@inproceedings{cartella2023open,
  title={{OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data}},
  author={Cartella, Giuseppe and Baldrati, Alberto and Morelli, Davide and Cornia, Marcella and Bertini, Marco and Cucchiara, Rita},
  booktitle={Proceedings of the International Conference on Image Analysis and Processing},
  year={2023}
}

This work has partially been supported by the European Commission under the PNRR-M4C2 (PE00000013) project "FAIR - Future Artificial Intelligence Research" and the European Horizon 2020 Programme (grant number 101004545 - ReInHerit), and by the PRIN project "CREATIVE: CRoss-modal understanding and gEnerATIon of Visual and tExtual content" (CUP B87G22000460001), co-funded by the Italian Ministry of University.

All material is available under Creative Commons BY-NC 4.0. You can use, redistribute, and adapt the material for non-commercial purposes, as long as you give appropriate credit by citing our paper and indicate any changes you've made.