README.md

metadata

license: cc-by-nc-sa-4.0
dataset_info:
  features:
    - name: image
      dtype: image
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 1363424468
      num_examples: 14904
  download_size: 1328309729
  dataset_size: 1363424468
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
task_categories:
  - text-to-image
language:
  - en
size_categories:
  - 10K<n<100K
images_reference:
  - KREAM (https://kream.co.kr/)
pretty_name: KREAM Product Blip Capitions
tags:
  - fashion
  - cloth
  - computer-vision

KREAM Product Blip Captions Dataset Information

KREAM Product Blip Captions Dataset is a dataset card for finetuning a text-to-image generative model collected from KREAM, one of the best online-resell market in Korea.

This dataset consists of 'image' and 'text' key pairs. The format of 'text' is 'category (e.g. outer), product original name (e.g. The North Face 1996 Eco Nuptse Jacket Black), blip captions (e.g. a photography of the north face black down jacket)'.

You can easily construct this dataset and finetune stable diffusion from scratch using easy-finetuning-stable-diffusion.

Usage

from datasets import load_dataset

dataset = load_dataset("hahminlew/kream-product-blip-captions", split="train")
sample = dataset[0]
display(sample["image"].resize((256, 256)))
print(sample["text"])

outer, The North Face 1996 Eco Nuptse Jacket Black, a photography of the north face black down jacket

Application

You can inference the finetuned Stable Diffusion XL with LoRA based on the dataset here: hahminlew/sdxl-kream-model-lora-2.0

Citation

If you use KREAM Product Dataset in your research or projects, please cite it as:

@misc{lew2023kream,
      author = {Lew, Hah Min},
      title = {KREAM Product BLIP Captions},
      year={2023},
      howpublished= {\url{https://huggingface.co/datasets/hahminlew/kream-product-blip-captions/}}
}