Phips/lucid-cc0-v2

LUCID-CC0 v2: Large-Scale Curated CC0 Training Dataset for Single-Image Super-Resolution

A large-scale, high-quality training dataset for single-image super-resolution (SISR), filtered from nyuuzyou/pxhere using the LUCID filtering pipeline. All source images are CC0-licensed (public domain).

Overview

Intended Use

This dataset is designed for training SISR models from scratch, particularly large transformer-based architectures that are data-hungry:

• —HAT (Hybrid Attention Transformer)
• —HAT-L (Large variant)
• —SwinIR
• —RealESRGAN / traiNNer-redux
• —Diffusion-based super-resolution models
• —Any new architecture that benefits from diverse, high-quality training data

Recommended Training Strategy

This dataset is part of a three-stage training pipeline:

Dataset Structure

lucid-cc0-v2/
├── train/
│   ├── 000/          # ≤10,000 tiles per subdirectory
│   │   ├── 00000.png
│   │   ├── 00001.png
│   │   └── ...
│   ├── 001/
│   └── ...
├── LR/
│   ├── x2/           # Bicubic downscaled ×2 (MATLAB-compatible)
│   └── x4/           # Bicubic downscaled ×4 (MATLAB-compatible)
├── batch_manifest.json
├── lineage_batch_*.csv
└── DATASET_NOTES.md

Filtering Pipeline

Images were filtered using LUCID with the following stages:

1. 1.Signal filter — Removes low-information images (blurry, overexposed, underexposed, low-contrast)
2. 2.ICNet complexity scoring — Neural network estimates perceptual complexity; tiles below threshold are removed
3. 3.Multiscale tiling — Images tiled at multiple scales (1.0×, 0.75×, 0.5×, 0.25×) to capture both fine detail and global structure
4. 4.Deduplication — Perceptual cosine similarity deduplication (threshold 0.96) removes near-duplicate tiles
5. 5.Tile extraction — 256×256 PNG tiles saved with ≤10,000 files per subdirectory

Source Data

• —Repository: nyuuzyou/pxhere
• —Description: ~1.1M CC0 images from PxHere, stored as WebDataset tars
• —Content: Professional photography spanning landscapes, architecture, nature, objects, and more
• —License: CC0-1.0 (public domain)

Bicubic Downscaling

LR (low-resolution) images are provided alongside HR tiles, downscaled using MATLAB-compatible bicubic interpolation (a = -0.5 anti-aliased cubic kernel). This matches the standard used in SISR benchmarks (Urban100, Set5, Set14, etc.) and ensures comparable PSNR/SSIM values.

Scale factors: ×2 and ×4.

Lineage

Each batch produces a lineage_batch_*.csv file tracking per-image complexity scores and tile counts for reproducibility.

Citation

If you use this dataset, please cite:

@dataset{lucid_cc0_v2,
  title={LUCID-CC0 v2: Large-Scale Curated CC0 Training Dataset for SISR},
  author={Phips},
  year={2026},
  license={CC0-1.0},
  url={https://huggingface.co/datasets/Phips/lucid-cc0-v2}
}

Acknowledgments

• —Source images from PxHere (CC0)
• —Filtering powered by LUCID
• —Inspired by the SISR community's need for large-scale, ethically-sourced training data