joyfromtinkerterror/beautyproducts

The Beauty API: 180k+ Skincare & Cosmetics Products Dataset

The world’s most comprehensive, normalized products database for beauty-tech and market research.

This repository contains a structured sample of The Beauty API. While most beauty datasets are messy scrapes, this is a production-ready asset built on a research-backed backbone, normalized for day-one integration into recommendation engines, e-commerce storefronts, and AI models.

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📊 Dataset Overview (as of 17 August 2026)

• Total Records: 180,000+ Unique Products, their functional classifications, ingredients list and dermatological safety
• Brand Coverage: 20,000+ Global Brands (e.g., Paula's Choice, CeraVe, The Ordinary, Augustinus Bader)
• Image Archive: 18GB of high-resolution, ID-mapped imagery (Standardized white backgrounds)
• Barcodes: GTIN-14 codes (UPC/EAN, globally resolvable ranges only) on ~44% of products, as a per-product gtin14 list
• Formats: Interchangeable CSV and JSONL

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💡 Data Capabilities

1. Ingredients Intelligence & Safety

Every product is parsed into a rich, structured object to bypass months of data wrangling.

• Full INCI breakdown: Each ingredient carries its on-label name, common aliases (other_names), and functional roles (functions, e.g. "solvent," "antioxidant," "soothing"), in exact on-pack order with label position preserved.
• Chemical identifiers: Regulatory-grade CAS, EC, IUPAC, and Ph. Eur. names per ingredient for enterprise R&D and compliance work.
• Dermatological Safety: Research-backed comedogenic ratings (0–5), irritancy scores (0–5), and editorial expert ratings at the ingredient level.
• Free-from flags: Boolean columns for instant filtering — contains_fragrance, contains_drying_alcohol, contains_parabens, contains_sulfates, contains_silicones.
• Product classification: Product category and brand origin for segmentation.

2. Machine Learning & Computer Vision

A pre-labeled training set for high-accuracy visual recognition and retail audits.

• Branding Analysis: Train multimodal models to decode the correlation between packaging design and ingredient quality.
• Visual Search: Use the 18GB mapped archive to build "Point-and-Know" camera features that bypass the need for barcodes.
• Retail Auditing: Power automated shelf-scanners to identify 180,000+ SKUs in real-world lighting conditions.

3. E-commerce Enrichment (B2B)

Metadata designed to boost conversion and lower return rates for indie e-com and retail.

• Barcode Matching: Join the catalogue to your own inventory or a scanner app on GTIN/UPC/EAN via the gtin14 list.
• Page Enrichment: Generate SEO-rich product descriptions and ingredient glossaries.
• Trust Signals: Implement safety badges and personalized skin-type matching engines.

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Technical Schema

The dataset is delivered in interchangeable CSV and JSONL formats.

Product fields

Field	Type	Nullable	Description
id	integer	no	Stable unique product id. Matches the image filename (<id>.jpeg). Ids are not strictly sequential; gaps reflect products dropped during clean.
brand	string	no	Brand / manufacturer name.
name	string	no	Product name. May contain non-ASCII characters, punctuation, or unicode whitespace.
image_name	string	yes	Filename of the product image (<id>.jpeg), or null.
gtin14	array<string>	no	Barcodes known for this product, each as a canonical 14-digit GTIN (left-zero-padded, so UPC-A 782446192371 is 00782446192371); codes the product primarily owns first, then ascending. Includes unit codes and codes of sibling variants of the same line (shade/scent/size). Only globally resolvable GS1 ranges (restricted in-store/coupon ranges excluded). [] when none known. In CSV this is the JSON-array text in one quoted column.
category	string	yes	Product category (e.g. skincare).
origin	string	yes	Brand country of origin, or null.
contains_fragrance	boolean	no	Formulation contains fragrance/parfum.
contains_drying_alcohol	boolean	no	Contains a drying (simple) alcohol.
contains_parabens	boolean	no	Contains parabens.
contains_sulfates	boolean	no	Contains sulfates.
contains_silicones	boolean	no	Contains silicones.
ingredients_simple	array<string>	yes	Flat ordered list of on-label ingredient names. In CSV this is a `
ingredients	array<object>	yes	Rich ingredient records, in on-pack order. In CSV this is a JSON-encoded string inside one quoted column. Per-ingredient fields below.

ingredients[] object fields

Field	Type	Nullable	Description
position	integer	no	0-based slot in the on-pack INCI list.
label_name	string	no	Exact on-label text for this ingredient on this product.
concentration	string	yes	Stated concentration, if printed.
ingredient_id	integer	no	Canonical ingredient id — the same chemical shares one id across products.
name	string	no	Canonical ingredient name.
rating	string	yes	Editorial rating bucket: one of direct actives, supporting actives, sensitizers, or null (unrated, the majority of ingredients).
irritancy	string	yes	Irritancy on a 0–5 scale (higher = more irritating), or null. A string because values come as points or ranges ("0", "3", "0-3").
comedogenicity	string	yes	Pore-clogging potential on a 0–5 scale (higher = more comedogenic), or null. Same point/range string encoding as irritancy.
functions	array<string>	yes	Functional roles (e.g. solvent, antioxidant).
cas_number	string	yes	CAS registry number.
ec_number	string	yes	EC number.
ph_eur_name	string	yes	European Pharmacopoeia name.
iupac_name	string	yes	IUPAC name.
other_names	string	yes	Known aliases / synonyms.
category	string	yes	Flag category that drives the product-level contains_* booleans: one of fragrance, drying_alcohol, parabens, sulfates, silicones, or null.

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Licensing

This sample dataset is provided under the CC BY-NC 4.0 license for non-commercial research and evaluation.

Commercial Access: For production e-commerce, commercial AI training, or retail audit tools, a commercial license is required. Visit thebeautyapi.com for licensing details and full dataset downloads.