dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 932793797
num_examples: 24791
- name: test
num_bytes: 120168332
num_examples: 3061
- name: validation
num_bytes: 107180687
num_examples: 2755
download_size: 1147593917
dataset_size: 1160142816
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
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This is the huggingface format of : https://data.caltech.edu/records/nyy15-4j048. Please cite the original author of the dataset
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BibTeX:
[ @misc{griffin_holub_perona_2022, title={Caltech 256}, DOI={10.22002/D1.20087}, abstractNote={We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category. Caltech-256 is collected in a similar manner with several improvements: a) the number of categories is more than doubled, b) the minimum number of images in any category is increased from 31 to 80, c) artifacts due to image rotation are avoided and d) a new and larger clutter category is introduced for testing background rejection. We suggest several testing paradigms to measure classification performance, then benchmark the dataset using two simple metrics as well as a state-of-the-art spatial pyramid matching algorithm. Finally we use the clutter category to train an interest detector which rejects uninformative background regions.}, publisher={CaltechDATA}, author={Griffin, Gregory and Holub, Alex and Perona, Pietro}, year={2022}, month={Apr} }]
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