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}
}