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())

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()

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()

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}
}