scripts/README.md

YFCC50K Photo Corpus Scripts

Pipeline for curating, fetching, and uploading the Lightcella photo corpus from the YFCC100M dataset.

Pipeline

Step 1: Curate (curate_manifest.py)

Selects 50k photos from YFCC100M with date/GPS/resolution stratification.

Requires:

• YFCC100M SQLite database (~65GB): yfcc100m_dataset.sql
• MediaEval placing subset files (downloaded automatically via AWS CLI)
• Python packages: yfcc100m, opencv-python-headless, awscli

Three phases run sequentially:

1. Build metadata -- scans YFCC SQLite for MediaEval placing hashes, extracts GPS/date/Flickr credentials
2. Select subset -- stratified sampling by date, GPS, faces, resolution tiers
3. Upgrade resolution -- probes Flickr for higher-res versions, assigns fetch tiers

python curate_manifest.py --db /path/to/yfcc100m_dataset.sql --out-dir ./output

Output: manifest.tsv (the same format shipped in this repo).

Step 2: Fetch (fetch_flickr.py)

Downloads Flickr originals for manifest entries. Resumable, rate-limit aware.

python fetch_flickr.py --manifest manifest.tsv --out-dir ./photos

Features:

• Classifies Flickr users as alive/dead/unsampled for efficient probing
• Validates JPEG dimensions (skips <501px max edge)
• Restores file mtime from manifest date (fallback for EXIF-less photos)
• Automatic retry with backoff on 429s

Step 3: Upload (upload_hf.py)

Creates ~1GB tar shards and uploads them to HuggingFace.

python upload_hf.py --src-dir ./photos --repo lightcella/photo-corpus --prefix yfcc

Features:

• Resumable (skips already-uploaded shards)
• Deletes local tar after successful upload to save disk