Datasets:
metadata
license: mit
task_categories:
- text-classification
- feature-extraction
- image-feature-extraction
- image-classification
language:
- en
tags:
- not-for-all-audiences
pretty_name: Perfume dataset
size_categories:
- 10K<n<100K
Perfume dataset, over 26k perfumes
Dataset Description
RAW Perfume dataset (2024 Year), over 26k perfumes. Images, ingredients, description, etc.
How to use
from huggingface_hub import hf_hub_download
# download raw archive file
zip_file = hf_hub_download(
repo_id='doevent/perfume',
repo_type='dataset',
filename='images.zip',
)
import zipfile
# extract files to your directory
dataset_dir = 'images'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
Dataset Structure
RAW data: SQLlite, JSON, CSV
SQL:
id,
brand TEXT,
name_perfume TEXT,
family TEXT,
subfamily TEXT,
fragrances TEXT,
ingredients TEXT,
origin TEXT,
gender TEXT,
years TEXT,
description TEXT,
image_name TEXT,
image_id TEXT
JSON:
[
{
"brand": "Fiorucci",
"name_perfume": "Wallstreet",
"family": "FLORAL",
"subfamily": "AMBERY (ORIENTAL)",
"fragrances": "Floral Amber Fresher",
"ingredients": [
"Jasmine",
"Lemon",
"Spicy Notes",
"Gardenia",
"Rose",
"Bergamot",
"Mint",
"Pepper"
],
"origin": "Brazil",
"gender": "Male",
"years": "2015",
"description": "A modern aroma, ideal for urban men and that highlights their strengths in everyday life.",
"image_name": "jsunc6exf3hcx3bfbnx0jw5f2s4zske1770lzwplo5hn2v4ky2qh4lxan392-w500-q85.jpg"
},
]
Dataset Creation
01.05.2024