Tiny ImageNet Dataset

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Visualization of the Tiny ImageNet dataset in the Deep Lake UI

Tiny ImageNet dataset

What is Tiny ImageNet Dataset?

In Tiny ImageNet, there are 100,000 images divided up into 200 classes. Every image in the dataset is downsized to a 64×64 colored image. For every class, there are 500 training images, 50 validating images, and 50 test images.

Download Tiny ImageNet Dataset in Python

Instead of downloading the Tiny ImageNet dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load Tiny ImageNet Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/tiny-imagenet-train")

Load Tiny ImageNet Dataset Testing Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/tiny-imagenet-test")

Load Tiny ImageNet Dataset Validation Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/tiny-imagenet-validation")

Tiny ImageNet Dataset Structure

Tiny ImageNet Data Fields

image: tensor containing the image.
labels: tensor to identify an object in the image
boxes: tensor to identify the object using bounding boxes.

Tiny ImageNet Data Splits

The TinyImageNet dataset training set is composed of 100,000 images.
The TinyImageNet dataset testing set is composed of 10,000 images.
The TinyImageNet dataset validation set is composed of 10,000 images.

How to use Tiny ImageNet Dataset with PyTorch and TensorFlow in Python

Train a model on Tiny ImageNet dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)

Train a model on Tiny ImageNet dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about Tiny ImageNet Dataset

Tiny ImageNet Dataset Description

Homepage: https://www.kaggle.com/c/tiny-imagenet
Repository: https://github.com/rmccorm4/Tiny-Imagenet-200
Paper: Introduced by Le et al. in Tiny imagenet visual recognition challenge
Point of Contact: N/A

Tiny ImageNet Dataset Curators

Ya Le and Xuan S. Yang

Tiny ImageNet Dataset Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

Tiny ImageNet Dataset Citation Information

				
					@inproceedings{Le2015TinyIV,
  title={Tiny ImageNet Visual Recognition Challenge},
  author={Ya Le and Xuan S. Yang},
  year={2015}
}

Tiny ImageNet Dataset FAQs

What is the Tiny ImageNet dataset for Python?

The Tiny ImageNet dataset is a visual database often used in visual object recognition software research. The Tiny ImageNet dataset is a modified subset of the original ImageNet dataset. The Tiny ImageNet dataset has 800 fewer classes than the ImageNet dataset, with 100,000 training examples and 10,000 validation examples.

How can I use Tiny ImageNet dataset in PyTorch or TensorFlow?

You can stream the Tiny ImageNet dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to train a model on a tiny imagenet dataset with PyTorch in Python or train a model on a tiny imagenet dataset with TensorFlow in Python.