metadata

size_categories:
  - 1K<n<10K
task_categories:
  - depth-estimation
  - image-segmentation
paperswithcode_id: nyuv2
tags:
  - depth-estimation
  - semantic-segmentation
dataset_info:
  features:
    - name: image
      dtype: image
    - name: depth
      dtype:
        array2_d:
          shape:
            - 640
            - 480
          dtype: float32
    - name: label
      dtype:
        array2_d:
          shape:
            - 640
            - 480
          dtype: int32
    - name: scene
      dtype: string
    - name: scene_type
      dtype: string
    - name: accelData
      sequence: float32
      length: 4
  splits:
    - name: train
      num_bytes: 4096489803
      num_examples: 1449
  download_size: 2972037809
  dataset_size: 4096489803

NYU Depth Dataset V2

This is an unofficial Hugging Face downloading script of the NYU Depth Dataset V2. It downloads the data from the original source and converts it to the Hugging Face format.

This dataset contains the 1449 densely labeled pairs of aligned RGB and depth images.

Dataset Description

Homepage: NYU Depth Dataset V2
Paper: Indoor Segmentation and Support Inference from RGBD Images

Official Description

The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. It features:

1449 densely labeled pairs of aligned RGB and depth images
464 new scenes taken from 3 cities
407,024 new unlabeled frames
Each object is labeled with a class and an instance number (cup1, cup2, cup3, etc)

This dataset is useful for various computer vision tasks, including depth estimation, semantic segmentation, and instance segmentation.

Usage

from datasets import load_dataset

dataset = load_dataset("0jl/NYUv2", trust_remote_code=True, split="train")

Common Errors

fsspec.exceptions.FSTimeoutError

Can occur for datasets==3.0 when the download takes more than 5 minutes. This increases the timeout to 1 hour:

import datasets, aiohttp
dataset = datasets.load_dataset(
    "0jl/NYUv2",
    trust_remote_code=True,
    split="train",
    storage_options={'client_kwargs': {'timeout': aiohttp.ClientTimeout(total=3600)}}
)

Dataset Structure

The dataset contains only one training split with the following features:

image: RGB image (PIL.Image.Image, shape: (640, 480, 3))
depth: Depth map (2D array, shape: (640, 480), dtype: float32)
label: Semantic segmentation labels (2D array, shape: (640, 480), dtype: int32)
scene: Scene name (string)
scene_type: Scene type (string)
accelData: Acceleration data (list, shape: (4,), dtype: float32)

Citation Information

If you use this dataset, please cite the original paper:

@inproceedings{Silberman:ECCV12,
  author    = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus},
  title     = {Indoor Segmentation and Support Inference from RGBD Images},
  booktitle = {Proceedings of the European Conference on Computer Vision},
  year      = {2012}
}