lance-format/flickr30k-lance

Flickr30k (Lance Format)

Lance-formatted version of Flickr30k (re-distributed via lmms-lab/flickr30k) — 31,783 images, each paired with 5 human-written captions, with CLIP image and text embeddings stored inline and pre-built ANN indices on both.

Key features

• Inline images — full JPEG bytes per row.
• Pre-computed CLIP embeddings for both image and caption text — IVF_PQ indices on both columns let you do cross-modal retrieval (image→caption or caption→image) without any model at query time.
• Full-text inverted index on the canonical caption.
• Self-contained: no sidecar files or external image downloads.

Schema

Column	Type	Notes
id	int64	Row index
image	large_binary	Inline JPEG bytes
image_id	string	Original Flickr image id
filename	string	Original filename (e.g. 1000092795.jpg)
captions	list<string>	All 5 captions for the image
caption	string	First caption — used as canonical text for FTS / quick browsing
image_emb	fixed_size_list<float32, 512>	CLIP image embedding (cosine-normalized)
text_emb	fixed_size_list<float32, 512>	CLIP text embedding of the canonical caption

Pre-built indices

• IVF_PQ on image_emb — metric=cosine
• IVF_PQ on text_emb — metric=cosine (cross-modal retrieval works out of the box)
• INVERTED on caption
• BTREE on image_id

Splits

A single train.lance table containing all 31,783 rows (the lmms-lab/flickr30k redistribution exposes them as a single split). The original train/val/test labels are not preserved in the source parquet.

Load with Lance

import lance

ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
print(ds.count_rows(), ds.schema.names, ds.list_indices())

Cross-modal text→image search

import lance
import pyarrow as pa
import open_clip
import torch

# 1. Encode the query text once with the same CLIP model used at conversion.
model, _, _ = open_clip.create_model_and_transforms("ViT-B-32", pretrained="laion2b_s34b_b79k")
tokenizer = open_clip.get_tokenizer("ViT-B-32")
model = model.eval().cuda().half()
with torch.no_grad():
    q = model.encode_text(tokenizer(["a man surfing at sunset"]).cuda())
    q = (q / q.norm(dim=-1, keepdim=True)).float().cpu().numpy()[0]

ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
emb_field = ds.schema.field("image_emb")
query = pa.array([q.tolist()], type=emb_field.type)

# 2. Nearest-neighbour search against the image embedding index.
hits = ds.scanner(
    nearest={"column": "image_emb", "q": query[0], "k": 10, "nprobes": 16, "refine_factor": 30},
    columns=["image_id", "caption"],
).to_table().to_pylist()
for h in hits:
    print(h)

Image→caption (image-to-text retrieval)

ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
ref = ds.take([0], columns=["image_emb", "caption"]).to_pylist()[0]
emb_field = ds.schema.field("text_emb")
query = pa.array([ref["image_emb"]], type=emb_field.type)
neighbors = ds.scanner(
    nearest={"column": "text_emb", "q": query[0], "k": 10},
    columns=["caption"],
).to_table().to_pylist()

Full-text search on captions

import lance
ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
hits = ds.scanner(
    full_text_query="dog playing in the snow",
    columns=["image_id", "caption"],
    limit=10,
).to_table().to_pylist()

Working with images

from pathlib import Path
import lance
ds = lance.dataset("hf://datasets/lance-format/flickr30k-lance/data/train.lance")
row = ds.take([0], columns=["image", "filename"]).to_pylist()[0]
Path(row["filename"]).write_bytes(row["image"])

Why Lance?

• One dataset carries images + image embeddings + text embeddings + indices — no sidecar files.
• On-disk vector and full-text indices live next to the data, so search works on local copies and on the Hub.
• Schema evolution: add columns (new captions, alternate embeddings, moderation labels) without rewriting the data.

Source & license

Converted from lmms-lab/flickr30k, which is itself a parquet redistribution of the original Flickr30k corpus. Original images come from Flickr; review the Flickr30k licensing terms before redistribution.

Citation

@article{young2014image,
  title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions},
  author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia},
  journal={Transactions of the Association for Computational Linguistics},
  volume={2},
  pages={67--78},
  year={2014}
}