README.md

---
license: cc-by-4.0
task_categories:
- object-detection
- image-feature-extraction
language:
- en
tags:
- coco
- coco-2017
- object-detection
- bounding-boxes
- lance
- clip-embeddings
pretty_name: coco-detection-2017-lance
size_categories:
- 100K<n<1M
---
# COCO 2017 Object Detection (Lance Format)

Lance-formatted version of the [COCO 2017 object detection benchmark](https://cocodataset.org/) — sourced from [`detection-datasets/coco`](https://huggingface.co/datasets/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

```python
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())
```

## Load with LanceDB

These tables can also be consumed by [LanceDB](https://lancedb.github.io/lancedb/), the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.

```python
import lancedb

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

> **Tip — for production use, download locally first.**
> ```bash
> hf download lance-format/coco-detection-2017-lance --repo-type dataset --local-dir ./coco-detection-2017-lance
> ```

## Read one annotated image

```python
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)

```python
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()
```

### Filter by classes with LanceDB

```python
import lancedb

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

rows = (
    tbl.search()
    .where("array_has_all(categories_present, ['person', 'frisbee'])")
    .select(["image_id", "category_names"])
    .limit(10)
    .to_list()
)

busy = (
    tbl.search()
    .where("num_objects >= 5")
    .select(["image_id", "num_objects"])
    .limit(10)
    .to_list()
)
```

## Visual similarity search

```python
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()
```

### LanceDB visual similarity search

```python
import lancedb

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

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

results = (
    tbl.search(query_embedding)
    .metric("cosine")
    .select(["image_id", "category_names"])
    .limit(5)
    .to_list()
)
```

## 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`](https://huggingface.co/datasets/detection-datasets/coco). COCO annotations are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/); the underlying images are subject to Flickr terms of service. See the [COCO Terms of Use](https://cocodataset.org/#termsofuse) 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}
}
```