lance-format/kitti-2d-detection-lance

KITTI 2D Object Detection (Lance Format)

Lance-formatted version of the KITTI 2D Object Detection benchmark — 7,481 training images from the KITTI Vision Benchmark Suite with 2D bounding boxes plus the full 3D-box / observation-angle metadata. Sourced from nateraw/kitti so no manual signup or download from cvlibs.net is required.

KITTI is the canonical autonomous-driving 2D / 3D detection benchmark — useful for AV perception research, robust real-world benchmarking, and as a small-scale companion to nuScenes / Waymo.

Splits

Split	Rows
train.lance	7,481

(The test split has no labels published, so we omit it. Add it back via --splits train test if you want the unlabeled images as well.)

Schema

Column	Type	Notes
id	int64	Row index within split
image	large_binary	Inline JPEG bytes (re-encoded from the source PNG)
bboxes	list<list<float32, 4>>	2D box per object — [left, top, right, bottom] in pixel coords
alphas	list<float32>	Observation angle (radians, KITTI convention)
dimensions	list<list<float32, 3>>	3D box (h, w, l) in metres
locations	list<list<float32, 3>>	3D centre (x, y, z) in camera coords (metres)
rotation_y	list<float32>	Yaw angle in camera coords (radians)
occluded	list<int8>	KITTI occlusion flag (0=visible, 1=partly, 2=largely, 3=unknown)
truncated	list<float32>	Truncation fraction (0.0-1.0)
types	list<string>	Class name per object (e.g. Car, Pedestrian, Cyclist, DontCare)
num_objects	int32	Number of annotated objects
types_present	list<string>	Deduped class names — feeds the LABEL_LIST index
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 num_objects
• LABEL_LIST on types_present

Quick start

import lance

ds = lance.dataset("hf://datasets/lance-format/kitti-2d-detection-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/kitti-2d-detection-lance/data")
tbl = db.open_table("train")
print(f"LanceDB table opened with {len(tbl)} frames")

Read a frame with annotations

import io
import lance
from PIL import Image, ImageDraw

ds = lance.dataset("hf://datasets/lance-format/kitti-2d-detection-lance/data/train.lance")
row = ds.take([0], columns=["image", "bboxes", "types"]).to_pylist()[0]

img = Image.open(io.BytesIO(row["image"])).convert("RGB")
draw = ImageDraw.Draw(img)
for (l, t, r, b), cls in zip(row["bboxes"], row["types"]):
    if cls == "DontCare":
        continue
    draw.rectangle([l, t, r, b], outline="lime", width=2)
    draw.text((l + 4, t + 2), cls, fill="lime")
img.save("kitti.jpg")

Filter by classes

import lance
ds = lance.dataset("hf://datasets/lance-format/kitti-2d-detection-lance/data/train.lance")

# Frames containing both a Car and a Cyclist (LABEL_LIST index makes this fast).
both = ds.scanner(
    filter="array_has_all(types_present, ['Car', 'Cyclist'])",
    columns=["id", "types_present"],
    limit=10,
).to_table()

# Frames with at least 10 objects (for crowded-scene experiments).
crowded = ds.scanner(filter="num_objects >= 10", columns=["id"], limit=10).to_table()

Filter by classes with LanceDB

import lancedb

db = lancedb.connect("hf://datasets/lance-format/kitti-2d-detection-lance/data")
tbl = db.open_table("train")

both = (
    tbl.search()
    .where("array_has_all(types_present, ['Car', 'Cyclist'])")
    .select(["id", "types_present"])
    .limit(10)
    .to_list()
)

crowded = (
    tbl.search()
    .where("num_objects >= 10")
    .select(["id"])
    .limit(10)
    .to_list()
)

Visual similarity search

import lance
import pyarrow as pa

ds = lance.dataset("hf://datasets/lance-format/kitti-2d-detection-lance/data/train.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, "nprobes": 16, "refine_factor": 30},
    columns=["id", "types_present"],
).to_table().to_pylist()

LanceDB visual similarity search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/kitti-2d-detection-lance/data")
tbl = db.open_table("train")

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

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

Why Lance?

• One dataset for images + 2D + 3D annotations + embeddings + indices — no parallel image_2/ and label_2/ folders.
• On-disk vector and label-list indices live next to the data, so search and class-based filtering work on local copies and on the Hub.
• Schema evolution: add columns (LIDAR features, alternative embeddings, model predictions) without rewriting the data.

Source & license

Converted from nateraw/kitti. KITTI is released under the CC BY-NC-SA 3.0 license by Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago — non-commercial research use only. See the KITTI license page for details.

Citation

@inproceedings{geiger2012are,
  title={Are we ready for autonomous driving? The KITTI vision benchmark suite},
  author={Geiger, Andreas and Lenz, Philip and Urtasun, Raquel},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2012}
}