ksachdeva / tiny-imagenet-tfds

tiny-imagenet dataset downloader & reader using tensorflow_datasets (tfds) api

tiny-imagenet-tfds

Introduction

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/.

Salient Features

• 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.

Install

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.git

Usage

tensorflow_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)