Phips/lucid-cc0-v2

LUCID CC0 v2 — 256×256 SISR Training Dataset

A large-scale, high-quality dataset for training single image super-resolution (SISR) models. Filtered from nyuuzyou/pxhere (CC0-licensed photography) using the LUCID filtering pipeline.

Format: WebDataset .tar shards (~1 GB each). Optimized for streaming training.

Statistics

Metric	Value
Tiles	1,169,792
Resolution	256×256 PNG
Total size	~158 GB
Shards	146 tar files (~1 GB each)
Source images	~33,000
Source dataset	nyuuzyou/pxhere (CC0)
ICNet complexity threshold	≥ 0.6
CLIP-IQA quality threshold	≥ 0.3
Filtering speed	~307 t/s (RTX 3060)

Filtering Pipeline

Every tile passes a three-stage quality gate (implemented in lucid-sisr):

1. Signal filtering — entropy, Laplacian variance, gradient energy, blockiness, noise ratio, aliasing ratio. Removes flat/uninformative regions (sky, water, solid colors).

2. ICNet complexity scoring — neural network measures local image complexity. Tiles below 0.6 threshold are removed. Ensures the model learns from informative content.

3. CLIP-IQA quality filtering — CLIP-based quality assessment detects ringing/haloring artifacts from aggressive sharpening. Tiles below 0.3 threshold are removed. This is critical because SISR models faithfully reproduce ringing artifacts as "details."

Key insight: blur and noise don't hurt SISR (the LR is just a bicubic downscaled HR), but ringing IS problematic — the model learns to reproduce halos as features. CLIP-IQA is the best metric for this (173.7% separation between ringing and clean images, 3.7× better than next best).

See the filtering pipeline documentation for details on each metric and threshold.

Source

Images are sourced from PxHere under CC0 license — professional photography with permissive licensing. PxHere was chosen over other CC0 sources because:

• Pro photography (less social media sharpening bias)
• Diverse content (architecture, nature, portraits, objects)
• Clean originals (less aggressive post-processing)

Usage

Training from scratch

This dataset is designed for the first stage of a three-stage training pipeline:

Stage 1: Pretrain on this dataset (256×256, 1.17M tiles)
    ↓
Stage 2: Finetune on LUCID CC0 v2 HC (256×256, 193K tiles)
    ↓
Stage 3: Finetune-finetune on LUCID CC0 v2 HC 512 (512×512, 101K tiles)

Use with traiNNer-redux and the HAT model:

python -m traiNNer.train -opt configs/train/HAT/HAT_M_LUCID_Pretrain.yml

Loading with WebDataset

import webdataset as wds

dataset = (
    wds.WebDataset("hf://datasets/Phips/lucid-cc0-v2/shard-{00000..00145}.tar")
    .decode("pil")
    .to_tuple("png")
)
for image in dataset:
    # image is a PIL Image
    pass

Related Datasets

• Phips/lucid-cc0-v2-hc — High-complexity subset (256×256, 193K tiles). For Stage 2 finetuning.
• Phips/lucid-cc0-v2-hc-512 — High-complexity subset (512×512, 18K tiles). For Stage 3 finetune-finetuning.

License

CC0-1.0 (public domain). Source: PxHere.

Citation

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

Links

• Filtering pipeline: Phhofm/lucid-sisr
• Source dataset: nyuuzyou/pxhere