0jl/NYUv2

This repo relies on a data loading script, which is no longer supported with datasets>=4.0

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.

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}
}