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())

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()

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()

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}
}