shapes3d

• Visualization: Explore in Know Your Data

• Description:

3dshapes is a dataset of 3D shapes procedurally generated from 6 ground truth independent latent factors. These factors are floor colour, wall colour, object colour, scale, shape and orientation.

All possible combinations of these latents are present exactly once, generating N = 480000 total images.

Latent factor values

• floor hue: 10 values linearly spaced in [0, 1]
• wall hue: 10 values linearly spaced in [0, 1]
• object hue: 10 values linearly spaced in [0, 1]
• scale: 8 values linearly spaced in [0, 1]
• shape: 4 values in [0, 1, 2, 3]
• orientation: 15 values linearly spaced in [-30, 30]

We varied one latent at a time (starting from orientation, then shape, etc), and sequentially stored the images in fixed order in the images array. The corresponding values of the factors are stored in the same order in the labels array.

• Homepage: https://github.com/deepmind/3d-shapes

• Source code: tfds.image.Shapes3d

• Versions:

  • 2.0.0 (default): New split API (https://tensorflow.org/datasets/splits)

• Download size: 255.18 MiB

• Dataset size: Unknown size

• Auto-cached (documentation): Unknown

• Splits:

Split	Examples
'train'	480,000

• Feature structure:

FeaturesDict({
    'image': Image(shape=(64, 64, 3), dtype=tf.uint8),
    'label_floor_hue': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'label_object_hue': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'label_orientation': ClassLabel(shape=(), dtype=tf.int64, num_classes=15),
    'label_scale': ClassLabel(shape=(), dtype=tf.int64, num_classes=8),
    'label_shape': ClassLabel(shape=(), dtype=tf.int64, num_classes=4),
    'label_wall_hue': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'value_floor_hue': tf.float32,
    'value_object_hue': tf.float32,
    'value_orientation': tf.float32,
    'value_scale': tf.float32,
    'value_shape': tf.float32,
    'value_wall_hue': tf.float32,
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
image	Image	(64, 64, 3)	tf.uint8
label_floor_hue	ClassLabel		tf.int64
label_object_hue	ClassLabel		tf.int64
label_orientation	ClassLabel		tf.int64
label_scale	ClassLabel		tf.int64
label_shape	ClassLabel		tf.int64
label_wall_hue	ClassLabel		tf.int64
value_floor_hue	Tensor		tf.float32
value_object_hue	Tensor		tf.float32
value_orientation	Tensor		tf.float32
value_scale	Tensor		tf.float32
value_shape	Tensor		tf.float32
value_wall_hue	Tensor		tf.float32

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@misc{3dshapes18,
  title={3D Shapes Dataset},
  author={Burgess, Chris and Kim, Hyunjik},
  howpublished={https://github.com/deepmind/3dshapes-dataset/},
  year={2018}
}