- Description:
Caltech-101 consists of pictures of objects belonging to 101 classes, plus one
background clutter
class. Each image is labelled with a single object. Each
class contains roughly 40 to 800 images, totalling around 9k images. Images are
of variable sizes, with typical edge lengths of 200-300 pixels. This version
contains image-level labels only. The original dataset also contains bounding
boxes.
Homepage: http://www.vision.caltech.edu/Image_Datasets/Caltech101/
Source code:
tfds.image_classification.Caltech101
Versions:
3.0.0
: New split API (https://tensorflow.org/datasets/splits)3.0.1
(default) : Website URL update
Download size:
125.64 MiB
Dataset size:
132.86 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
6,084 |
'train' |
3,060 |
- Features:
FeaturesDict({
'image': Image(shape=(None, None, 3), dtype=tf.uint8),
'image/file_name': Text(shape=(), dtype=tf.string),
'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=102),
})
Supervised keys (See
as_supervised
doc):('image', 'label')
Citation:
@article{FeiFei2004LearningGV,
title={Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories},
author={Li Fei-Fei and Rob Fergus and Pietro Perona},
journal={Computer Vision and Pattern Recognition Workshop},
year={2004},
}
- Figure (tfds.show_examples):
- Examples (tfds.as_dataframe):