Tiny Imagenet is a scaled down version of ImageNet dataset. This dataset was created by folks at Stanford for their course http://cs231n.stanford.edu/.
- 200 image classes
- Training dataset of 100,000 images
- Validation dataset of 10,000 images
- Test dataset of 10,000 images.
- All images are of size 64×64.
Note that the labels for Test dataset have not been provided.
I have not yet published it on pypi yet so install it directly from github.
pip install git+https://github.com/ksachdeva/tiny-imagenet-tfds.gittensorflow_datasets is a python package that provides support for downloading, preparing and
constructing a tf.data.Dataset. See more information at -
https://www.tensorflow.org/datasets/overview
This package provides the support for tiny-imagenet dataset.
import os
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from tiny_imagenet import TinyImagenetDataset
# optional
tf.compat.v1.enable_eager_execution()
tiny_imagenet_builder = TinyImagenetDataset()
# this call (download_and_prepare) will trigger the download of the dataset
# and preparation (conversion to tfrecords)
#
# This will be done only once and on next usage tfds will
# use the cached version on your host.
#
# You can pass optional argument to this method called
# DownloadConfig (https://www.tensorflow.org/datasets/api_docs/python/tfds/download/DownloadConfig)
# to customize the location where the dataset is downloaded, extracted and processed.
tiny_imagenet_builder.download_and_prepare()
train_dataset = tiny_imagenet_builder.as_dataset(split="train")
validation_dataset = tiny_imagenet_builder.as_dataset(split="validation")
assert(isinstance(train_dataset, tf.data.Dataset))
assert(isinstance(validation_dataset, tf.data.Dataset))
for a_train_example in train_dataset.take(5):
image, label, id = a_train_example["image"], a_train_example["label"], a_train_example["id"]
print(f"Image Shape - {image.shape}")
print(f"Label - {label.numpy()}")
print(f"Id - {id.numpy()}")
# print info about the data
print(tiny_imagenet_builder.info)