dmlab

• Description:

The Dmlab dataset contains frames observed by the agent acting in the DeepMind Lab environment, which are annotated by the distance between the agent and various objects present in the environment. The goal is to is to evaluate the ability of a visual model to reason about distances from the visual input in 3D environments. The Dmlab dataset consists of 360x480 color images in 6 classes. The classes are {close, far, very far} x {positive reward, negative reward} respectively.

• Homepage: https://github.com/google-research/task_adaptation

• Source code: tfds.image_classification.Dmlab

• Versions:

  • 2.0.1 (default): No release notes.

• Download size: 2.81 GiB

• Dataset size: 3.13 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	22,735
'train'	65,550
'validation'	22,628

• Feature structure:

FeaturesDict({
    'filename': Text(shape=(), dtype=string),
    'image': Image(shape=(360, 480, 3), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=6),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
filename	Text		string
image	Image	(360, 480, 3)	uint8
label	ClassLabel		int64

• Supervised keys (See as_supervised doc): ('image', 'label')

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@article{zhai2019visual,
        title={The Visual Task Adaptation Benchmark},
        author={Xiaohua Zhai and Joan Puigcerver and Alexander Kolesnikov and
               Pierre Ruyssen and Carlos Riquelme and Mario Lucic and
               Josip Djolonga and Andre Susano Pinto and Maxim Neumann and
               Alexey Dosovitskiy and Lucas Beyer and Olivier Bachem and
               Michael Tschannen and Marcin Michalski and Olivier Bousquet and
               Sylvain Gelly and Neil Houlsby},
                              year={2019},
                              eprint={1910.04867},
                              archivePrefix={arXiv},
                              primaryClass={cs.CV},
                              url = {https://arxiv.org/abs/1910.04867}
                          }