lance-format/coco-detection-2017-lance

COCO 2017 Object Detection (Lance Format)

Lance-formatted version of the COCO 2017 object detection benchmark — sourced from detection-datasets/coco — with 123,287 images and the full per-image list of bounding boxes, category labels, and CLIP image embeddings, all stored inline.

Why this version?

Object detection datasets typically split images, annotations, and embeddings across multiple files (often three different formats: JPEG, JSON, NumPy). Lance keeps all of it in one tabular dataset:

• one row per image,
• the JPEG bytes, the bounding box list, the category labels, and the CLIP image embedding all live as columns on the same row,
• IVF_PQ on the embedding column lets you do visual similarity search without leaving the dataset, and LABEL_LIST on categories_present lets you filter to "images containing a dog and a frisbee" in milliseconds.

Splits

Split	Rows
train.lance	117,000+
val.lance	4,950+

(Counts come from the detection-datasets/coco redistribution; box counts: ~860k train / ~37k val.)

Schema

Column	Type	Notes
id	int64	Row index within split
image	large_binary	Inline JPEG bytes
image_id	int64	COCO image id
width, height	int32	Image dimensions in pixels
bboxes	list<list<float32, 4>>	Each box is [x_min, y_min, x_max, y_max] in absolute pixel coords
categories	list<int32>	COCO 80-class id (0-79)
category_names	list<string>	Human-readable class name per object (e.g. person, dog, …)
areas	list<float32>	Bounding-box area (pixels²)
num_objects	int32	Number of annotated objects in the image
categories_present	list<string>	Deduped class names — feeds the LABEL_LIST index for fast filtering
image_emb	fixed_size_list<float32, 512>	OpenCLIP ViT-B-32 image embedding (cosine-normalized)

Pre-built indices

• IVF_PQ on image_emb — metric=cosine
• BTREE on image_id, num_objects
• LABEL_LIST on categories_present — supports array_has_any / array_has_all predicates

Quick start

import lance

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

Tip — for production use, download locally first.

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

Read one annotated image

import io
import lance
from PIL import Image, ImageDraw

ds = lance.dataset("hf://datasets/lance-format/coco-detection-2017-lance/data/val.lance")
row = ds.take([0], columns=["image", "bboxes", "category_names", "width", "height"]).to_pylist()[0]

img = Image.open(io.BytesIO(row["image"])).convert("RGB")
draw = ImageDraw.Draw(img)
for (x1, y1, x2, y2), name in zip(row["bboxes"], row["category_names"]):
    draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
    draw.text((x1 + 4, y1 + 4), name, fill="red")
img.save("annotated.jpg")

Filter by classes (LABEL_LIST index)

import lance
ds = lance.dataset("hf://datasets/lance-format/coco-detection-2017-lance/data/val.lance")

# Images that contain BOTH a person and a frisbee.
rows = ds.scanner(
    filter="array_has_all(categories_present, ['person', 'frisbee'])",
    columns=["image_id", "category_names"],
    limit=10,
).to_table().to_pylist()

# Images with at least 5 objects of any class.
busy = ds.scanner(
    filter="num_objects >= 5",
    columns=["image_id", "num_objects"],
    limit=10,
).to_table().to_pylist()

Visual similarity search

import lance
import pyarrow as pa

ds = lance.dataset("hf://datasets/lance-format/coco-detection-2017-lance/data/val.lance")
emb_field = ds.schema.field("image_emb")
ref = ds.take([0], columns=["image_emb"]).to_pylist()[0]["image_emb"]
query = pa.array([ref], type=emb_field.type)

neighbors = ds.scanner(
    nearest={"column": "image_emb", "q": query[0], "k": 5},
    columns=["image_id", "category_names"],
).to_table().to_pylist()

Why Lance?

• One dataset carries images + boxes + categories + areas + embeddings + indices — no JSON sidecars.
• On-disk vector and label-list indices live next to the data, so filters and ANN search work on local copies and on the Hub.
• Schema evolution: add columns (segmentation polygons, keypoints, panoptic ids, fresh embeddings) without rewriting the data.

Source & license

Converted from detection-datasets/coco. COCO annotations are released under CC BY 4.0; the underlying images are subject to Flickr terms of service. See 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}
}