tanganke/nyuv2

This is the NYUv2 dataset for scene understanding tasks. I downloaded the original data from the Tsinghua Cloud and transformed it into Huggingface Dataset. Credit to ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning.

Dataset Information

This data contains two splits: 'train' and 'val' (used as test dataset). Each sample in the dataset has 5 items: 'image', 'segmentation', 'depth', 'normal', and 'noise'. The noise is generated using torch.rand().

Usage

dataset = load_dataset('tanganke/nyuv2')
dataset = dataset.with_format('torch') # this will convert the items into `torch.Tensor` objects

this will return a DatasetDict:

DatasetDict({
    train: Dataset({
        features: ['image', 'segmentation', 'depth', 'normal', 'noise'],
        num_rows: 795
    })
    val: Dataset({
        features: ['image', 'segmentation', 'depth', 'normal', 'noise'],
        num_rows: 654
    })
})

The features:

{'image': Array3D(shape=(3, 288, 384), dtype='float32', id=None),
 'segmentation': Array2D(shape=(288, 384), dtype='int64', id=None),
 'depth': Array3D(shape=(1, 288, 384), dtype='float32', id=None),
 'normal': Array3D(shape=(3, 288, 384), dtype='float32', id=None),
 'noise': Array3D(shape=(1, 288, 384), dtype='float32', id=None)}