In this implementation, we'll use CIFAR-10, which is one of the most widely used datasets for object detection. So, let's start off by defining a helper class to download and extract the CIFAR-10 dataset, if it's not already downloaded:

cifar10_batches_dir_path = 'cifar-10-batches-py'

tar_gz_filename = 'cifar-10-python.tar.gz'

class DLProgress(tqdm):
    last_block = 0

    def hook(self, block_num=1, block_size=1, total_size=None):
        self.total = total_size
        self.update((block_num - self.last_block) * block_size)
        self.last_block = block_num

if not isfile(tar_gz_filename):
    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Python Images Batches') as pbar:
        urlretrieve(
            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
            tar_gz_filename,
            pbar.hook)

if not isdir(cifar10_batches_dir_path):
    with tarfile.open(tar_gz_filename) as tar:
        tar.extractall()
        tar.close()

After downloading and extracting the CIFAR-10 dataset, you will find out that it's already split into five batches. CIFAR-10 contains images for 10 categories/classes:

• airplane
• automobile
• bird
• cat
• deer
• dog
• frog
• horse
• ship
• truck

Before we dive into building the core of the network, let's do some data analysis and preprocessing.