lost_and_found

• Description:

The LostAndFound Dataset addresses the problem of detecting unexpected small obstacles on the road often caused by lost cargo. The dataset comprises 112 stereo video sequences with 2104 annotated frames (picking roughly every tenth frame from the recorded data).

The dataset is designed analogous to the 'Cityscapes' dataset. The datset provides: - stereo image pairs in either 8 or 16 bit color resolution - precomputed disparity maps - coarse semantic labels for objects and street

Descriptions of the labels are given here: http://www.6d-vision.com/laf_table.pdf

• Additional Documentation: Explore on Papers With Code

• Homepage: http://www.6d-vision.com/lostandfounddataset

• Source code: tfds.datasets.lost_and_found.Builder

• Versions:

  • 1.0.0 (default): No release notes.

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	1,203
'train'	1,036

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Citation:

@inproceedings{pinggera2016lost,
  title={Lost and found: detecting small road hazards for self-driving vehicles},
  author={Pinggera, Peter and Ramos, Sebastian and Gehrig, Stefan and Franke, Uwe and Rother, Carsten and Mester, Rudolf},
  booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2016}
}

lost_and_found/semantic_segmentation (default config)

• Config description: Lost and Found semantic segmentation dataset.

• Download size: 5.44 GiB

• Dataset size: 5.42 GiB

• Feature structure:

FeaturesDict({
    'image_id': Text(shape=(), dtype=string),
    'image_left': Image(shape=(1024, 2048, 3), dtype=uint8),
    'segmentation_label': Image(shape=(1024, 2048, 1), dtype=uint8),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
image_id	Text		string
image_left	Image	(1024, 2048, 3)	uint8
segmentation_label	Image	(1024, 2048, 1)	uint8

• Examples (tfds.as_dataframe):

lost_and_found/stereo_disparity

• Config description: Lost and Found stereo images and disparity maps.

• Download size: 12.16 GiB

• Dataset size: 12.22 GiB

• Feature structure:

FeaturesDict({
    'disparity_map': Image(shape=(1024, 2048, 1), dtype=uint8),
    'image_id': Text(shape=(), dtype=string),
    'image_left': Image(shape=(1024, 2048, 3), dtype=uint8),
    'image_right': Image(shape=(1024, 2048, 3), dtype=uint8),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
disparity_map	Image	(1024, 2048, 1)	uint8
image_id	Text		string
image_left	Image	(1024, 2048, 3)	uint8
image_right	Image	(1024, 2048, 3)	uint8

• Examples (tfds.as_dataframe):

lost_and_found/full

• Config description: Full Lost and Found dataset.

• Download size: 12.19 GiB

• Dataset size: 12.25 GiB

• Feature structure:

FeaturesDict({
    'disparity_map': Image(shape=(1024, 2048, 1), dtype=uint8),
    'image_id': Text(shape=(), dtype=string),
    'image_left': Image(shape=(1024, 2048, 3), dtype=uint8),
    'image_right': Image(shape=(1024, 2048, 3), dtype=uint8),
    'instance_id': Image(shape=(1024, 2048, 1), dtype=uint8),
    'segmentation_label': Image(shape=(1024, 2048, 1), dtype=uint8),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
disparity_map	Image	(1024, 2048, 1)	uint8
image_id	Text		string
image_left	Image	(1024, 2048, 3)	uint8
image_right	Image	(1024, 2048, 3)	uint8
instance_id	Image	(1024, 2048, 1)	uint8
segmentation_label	Image	(1024, 2048, 1)	uint8

• Examples (tfds.as_dataframe):

lost_and_found/full_16bit

• Config description: Full Lost and Found dataset.

• Download size: 34.90 GiB

• Dataset size: 35.05 GiB

• Feature structure:

FeaturesDict({
    'disparity_map': Image(shape=(1024, 2048, 1), dtype=uint8),
    'image_id': Text(shape=(), dtype=string),
    'image_left': Image(shape=(1024, 2048, 3), dtype=uint8),
    'image_right': Image(shape=(1024, 2048, 3), dtype=uint8),
    'instance_id': Image(shape=(1024, 2048, 1), dtype=uint8),
    'segmentation_label': Image(shape=(1024, 2048, 1), dtype=uint8),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
disparity_map	Image	(1024, 2048, 1)	uint8
image_id	Text		string
image_left	Image	(1024, 2048, 3)	uint8
image_right	Image	(1024, 2048, 3)	uint8
instance_id	Image	(1024, 2048, 1)	uint8
segmentation_label	Image	(1024, 2048, 1)	uint8

• Examples (tfds.as_dataframe):