RayyanAhmed9477/pexels-advertisement-photography

Pexels Advertisement & Product Photography Dataset

A curated dataset of 12,917 high-quality advertisement and product photography images sourced from Pexels, enriched with AI-generated captions for text-to-image model fine-tuning.

Dataset Overview

Pipeline

This dataset was built through a multi-stage pipeline:

1. Scraping (Pexels API)

• —40 curated search queries spanning product photography, commercial advertising, luxury brands, food, fashion, automotive, tech, and more
• —Up to 400 images per query via paginated API calls
• —12,927 unique images downloaded at medium resolution (940px wide)

2. Filtering

• —Resolution filter: Minimum 512px on shortest side (removes low-quality thumbnails)
• —Perceptual deduplication: pHash-based near-duplicate removal (hamming distance threshold)
• —Aesthetic scoring: CLIP-based aesthetic predictor scores each image
• —Result: 12,917 images retained (99.9% pass rate — Pexels quality is inherently high)

3. Caption Enrichment (VLM)

Each image was captioned by Qwen3-VL-8B with two complementary styles:

• —`caption_long`: Detailed advertising description (~50-100 words) describing composition, lighting, mood, product placement, and marketing context
• —`caption_short`: Concise tag-style caption (~10-20 words) with comma-separated descriptors for style, category, and key visual elements

This dual-caption approach enables stochastic caption sampling during training (e.g., 70% long / 30% short) for better prompt generalization.

Search Queries (40 Categories)

<details> <summary>Click to expand full query list</summary>

Product Photography:

• —product photography studio white background
• —cosmetics product shot professional
• —perfume bottle luxury commercial
• —jewelry product photography
• —luxury watch product shot
• —skincare product minimal
• —running shoes product
• —headphones technology product
• —handbag luxury product
• —sunglasses product photography
• —wine bottle product shot
• —smartphone product photography
• —beauty product flat lay
• —e-commerce product listing

Food & Beverage:

• —food photography commercial
• —coffee cup latte art
• —chocolate dessert food photography
• —beverage drink commercial
• —food advertisement menu

Automotive & Fashion:

• —car automotive photography
• —automobile car advertisement
• —fashion accessories product
• —fashion advertisement editorial
• —furniture modern interior design

Advertising & Commercial:

• —advertisement commercial campaign
• —brand advertisement poster
• —luxury brand advertisement
• —retail store advertisement display
• —billboard advertisement commercial
• —magazine advertisement layout
• —digital advertisement banner
• —commercial branding photography
• —marketing campaign visual
• —sale promotion advertisement
• —shopping advertisement retail
• —real estate advertisement property
• —tech gadget advertisement
• —fitness sports advertisement
• —beauty cosmetics advertisement campaign
• —holiday seasonal advertisement

</details>

Dataset Statistics

Usage

from datasets import load_dataset

# Load the dataset
ds = load_dataset("RayyanAhmed9477/pexels-advertisement-photography", split="train")

# Access an example
example = ds[0]
image = example["image"]          # PIL Image
long_caption = example["caption_long"]   # Detailed advertising description
short_caption = example["caption_short"] # Tag-style caption
score = example["aesthetic_score"]       # Aesthetic quality score

# For LoRA fine-tuning with stochastic captioning
import random
caption = long_caption if random.random() < 0.7 else short_caption

Use with Diffusers

This dataset is designed for fine-tuning text-to-image diffusion models (e.g., Stable Diffusion, FLUX, Qwen-Image) via LoRA:

from datasets import load_dataset

ds = load_dataset("RayyanAhmed9477/pexels-advertisement-photography", split="train")

# Example: prepare for diffusers training
for example in ds:
    image = example["image"]
    caption = example["caption_long"]  # or caption_short
    # ... your training pipeline

Schema

License & Attribution

• —Images are sourced from Pexels under the Pexels License
• —Pexels License permits free use for commercial and non-commercial purposes
• —No attribution required, but appreciated
• —AI-generated captions are provided as-is

Built With

• —Pexels API for image sourcing
• —Qwen3-VL-8B for caption generation
• —CLIP for aesthetic scoring
• —pHash for deduplication

Citation

@dataset{pexels_ad_photography_2026,
  title={Pexels Advertisement & Product Photography Dataset},
  author={Rayyan Ahmed},
  year={2026},
  url={https://huggingface.co/datasets/RayyanAhmed9477/pexels-advertisement-photography},
  note={12,917 images with dual AI captions for text-to-image fine-tuning}
}