lance-format/coco-captions-2017-lance

COCO Captions 2017 (Lance Format)

Lance-formatted version of the COCO Captions 2017 corpus, redistributed via lmms-lab/COCO-Caption2017. Each row is one image with 5–7 human-written captions, CLIP image embedding, and CLIP text embedding of the canonical caption — all stored inline.

Splits

Split	Rows
val.lance	5,000 (canonical COCO 2017 val set)
test.lance	40,700

The 2017 train split (118 k images, ~18 GB of source JPEGs) is intentionally not bundled here because the lmms-lab/COCO-Caption2017 redistribution does not include it. To extend with train, run coco_captions_2017/dataprep.py against your local COCO 2017 train mirror.

Schema

Column	Type	Notes
id	int64	Row index within split
image	large_binary	Inline JPEG bytes
image_id	string	COCO image id
filename	string	Original filename (e.g. 000000179765.jpg)
captions	list<string>	All 5–7 captions
caption	string	First caption — used as canonical text for FTS
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 and text_emb — metric=cosine
• INVERTED on caption
• BTREE on image_id

Quick start

import lance

ds = lance.dataset("hf://datasets/lance-format/coco-captions-2017-lance/data/val.lance")
print(ds.count_rows(), ds.schema.names)
print(ds.list_indices())

Load with LanceDB

These tables can also be consumed by LanceDB, the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.

import lancedb

db = lancedb.connect("hf://datasets/lance-format/coco-captions-2017-lance/data")
tbl = db.open_table("val")
print(f"LanceDB table opened with {len(tbl)} image-caption pairs")

Tip — for production use, download locally first.

hf download lance-format/coco-captions-2017-lance --repo-type dataset --local-dir ./coco-captions-2017-lance

Vector search examples

Cross-modal text→image:

import lance, open_clip, pyarrow as pa, torch

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 giraffe eating leaves"]).cuda())
    q = (q / q.norm(dim=-1, keepdim=True)).float().cpu().numpy()[0]

ds = lance.dataset("hf://datasets/lance-format/coco-captions-2017-lance/data/val.lance")
emb_field = ds.schema.field("image_emb")
hits = ds.scanner(
    nearest={"column": "image_emb", "q": pa.array([q.tolist()], type=emb_field.type)[0], "k": 10},
    columns=["image_id", "caption"],
).to_table().to_pylist()

LanceDB cross-modal text→image search

import lancedb, open_clip, torch

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 giraffe eating leaves"]).cuda())
    q = (q / q.norm(dim=-1, keepdim=True)).float().cpu().numpy()[0]

db = lancedb.connect("hf://datasets/lance-format/coco-captions-2017-lance/data")
tbl = db.open_table("val")

results = (
    tbl.search(q.tolist(), vector_column_name="image_emb")
    .metric("cosine")
    .select(["image_id", "caption"])
    .limit(10)
    .to_list()
)

Full-text search:

ds = lance.dataset("hf://datasets/lance-format/coco-captions-2017-lance/data/val.lance")
hits = ds.scanner(
    full_text_query="surfer riding a wave",
    columns=["image_id", "caption"],
    limit=10,
).to_table().to_pylist()

LanceDB full-text search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/coco-captions-2017-lance/data")
tbl = db.open_table("val")

results = (
    tbl.search("surfer riding a wave")
    .select(["image_id", "caption"])
    .limit(10)
    .to_list()
)

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, model predictions) without rewriting the data.

Source & license

Converted from lmms-lab/COCO-Caption2017. Original COCO 2017 annotations are released under CC BY 4.0; the underlying images are subject to Flickr terms of service. Please review the COCO Terms of Use before redistribution.

Citation

@inproceedings{lin2014microsoft,
  title={Microsoft COCO: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2014},
}