lance-format/oxford-pets-lance

Oxford-IIIT Pet (Lance Format)

Lance-formatted version of the Oxford-IIIT Pet dataset — 7,390 cat & dog photos across 37 breeds — sourced from pcuenq/oxford-pets.

Schema

Column	Type	Notes
id	int64	Row index
image	large_binary	Inline JPEG bytes
label_name	string	One of 37 breeds, underscore-spaced (british_shorthair, golden_retriever, …)
is_dog	bool	true for dog breeds, false for cat breeds
path	string?	Original filename in the source dataset
image_emb	fixed_size_list<float32, 512>	OpenCLIP ViT-B-32 embedding (cosine-normalized)

Pre-built indices

• IVF_PQ on image_emb — metric=cosine
• BITMAP on label_name and is_dog

Quick start

import lance
ds = lance.dataset("hf://datasets/lance-format/oxford-pets-lance/data/train.lance")
print(ds.count_rows(), ds.schema.names, 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/oxford-pets-lance/data")
tbl = db.open_table("train")
print(f"LanceDB table opened with {len(tbl)} images")

Filter — only dogs, only golden retrievers, etc.

import lance
ds = lance.dataset("hf://datasets/lance-format/oxford-pets-lance/data/train.lance")
dogs = ds.scanner(filter="is_dog = true", columns=["label_name"], limit=5).to_table()
goldens = ds.scanner(filter="label_name = 'golden_retriever'", columns=["id"], limit=5).to_table()

Filter with LanceDB

import lancedb

db = lancedb.connect("hf://datasets/lance-format/oxford-pets-lance/data")
tbl = db.open_table("train")
dogs = tbl.search().where("is_dog = true").select(["label_name"]).limit(5).to_list()
goldens = tbl.search().where("label_name = 'golden_retriever'").select(["id"]).limit(5).to_list()

Visual similarity search

import lance, pyarrow as pa
ds = lance.dataset("hf://datasets/lance-format/oxford-pets-lance/data/train.lance")
emb_field = ds.schema.field("image_emb")
ref = ds.take([0], columns=["image_emb", "label_name"]).to_pylist()[0]
neighbors = ds.scanner(
    nearest={"column": "image_emb", "q": pa.array([ref["image_emb"]], type=emb_field.type)[0], "k": 5},
    columns=["id", "label_name"],
).to_table().to_pylist()

LanceDB visual similarity search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/oxford-pets-lance/data")
tbl = db.open_table("train")

ref = tbl.search().limit(1).select(["image_emb", "label_name"]).to_list()[0]
query_embedding = ref["image_emb"]

results = (
    tbl.search(query_embedding)
    .metric("cosine")
    .select(["id", "label_name"])
    .limit(5)
    .to_list()
)

Source & license

Converted from pcuenq/oxford-pets. Released under CC BY-SA 4.0.

Citation

@inproceedings{parkhi2012cats,
  title={Cats and Dogs},
  author={Parkhi, Omkar M. and Vedaldi, Andrea and Zisserman, Andrew and Jawahar, C. V.},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2012}
}