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pet_finder

Dataset with images from 5 classes (see config name for information on the specific class)

Features

FeaturesDict({
    'PetID': Text(shape=(), dtype=tf.string),
    'attributes': FeaturesDict({
        'Age': Tensor(shape=(), dtype=tf.int64),
        'Breed1': Tensor(shape=(), dtype=tf.int64),
        'Breed2': Tensor(shape=(), dtype=tf.int64),
        'Color1': Tensor(shape=(), dtype=tf.int64),
        'Color2': Tensor(shape=(), dtype=tf.int64),
        'Color3': Tensor(shape=(), dtype=tf.int64),
        'Dewormed': Tensor(shape=(), dtype=tf.int64),
        'Fee': Tensor(shape=(), dtype=tf.int64),
        'FurLength': Tensor(shape=(), dtype=tf.int64),
        'Gender': Tensor(shape=(), dtype=tf.int64),
        'Health': Tensor(shape=(), dtype=tf.int64),
        'MaturitySize': Tensor(shape=(), dtype=tf.int64),
        'Quantity': Tensor(shape=(), dtype=tf.int64),
        'State': Tensor(shape=(), dtype=tf.int64),
        'Sterilized': Tensor(shape=(), dtype=tf.int64),
        'Type': Tensor(shape=(), dtype=tf.int64),
        'Vaccinated': Tensor(shape=(), dtype=tf.int64),
        'VideoAmt': Tensor(shape=(), dtype=tf.int64),
    }),
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=5),
})

Statistics

Split Examples
ALL 72,776
TRAIN 58,311
TEST 14,465

Urls

Supervised keys (for as_supervised=True)

(u'attributes', u'label')

Citation

@ONLINE {kaggle-petfinder-adoption-prediction,
    author = "Kaggle and PetFinder.my",
    title  = "PetFinder.my Adoption Prediction",
    month  = "april",
    year   = "2019",
    url    = "https://www.kaggle.com/c/petfinder-adoption-prediction/data/"
}