Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images and 50 test images.

Dataset Card for tiny-imagenet

  Dataset Summary

Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images.

  Languages

The class labels in the dataset are in English.

  Dataset Structure





  Data Instances

{ 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190, 'label': 15 }… See the full description on the dataset page: https://huggingface.co/datasets/zh-plus/tiny-imagenet.

The dataset used in the paper is CIFAR-10, CIFAR-100 and Tiny-Imagenet datasets.

Tiny ImageNet-C is an open-source data set comprising algorithmically generated corruptions applied to the Tiny ImageNet (ImageNet-200) test set comprising 200 classes following the concept of ImageNet-C. It was introduced by Hendrycks et al. ("Benchmarking Neural Network Robustness to Common Corruptions and Perturbations") and comprises 19 different corruptions (15 test corruptions and 4 validation corruptions) spanning 5 severity levels. This results in 200,000 images for the validation set and 750,000 images for the test set. For further information visit the original GitHub repository of ImageNet-C.

Dataset Card for Tiny-ImageNet-C

  Dataset Details





  Dataset Description

In Tiny ImageNet-C, there are 75,109 corrupted images derived from the original Tiny ImageNet dataset. The images are affected by two different corruption types at five severity levels.

License: CC BY 4.0

  Dataset Sources

Homepage: https://github.com/hendrycks/robustness Paper: Hendrycks, D., & Dietterich, T. (2019). Benchmarking neural network robustness to common… See the full description on the dataset page: https://huggingface.co/datasets/randall-lab/tiny-imagenet-c.

Dataset Description

A mini version of ImageNet-1k with 100 of 1000 classes present. Unlike some 'mini' variants this one includes the original images at their original sizes. Many such subsets downsample to 84x84 or other smaller resolutions.

  Data Splits





  Train

50000 samples from ImageNet-1k train split

  Validation

10000 samples from ImageNet-1k train split

  Test

5000 samples from ImageNet-1k validation split (all 50 samples per class)… See the full description on the dataset page: https://huggingface.co/datasets/timm/mini-imagenet.

mini-Imagenet is proposed by Matching Networks for One Shot Learning . In NeurIPS, 2016. This dataset consists of 50000 training images and 10000 testing images, evenly distributed across 100 classes.

Tiny ImageNet-R is a subset of the ImageNet-R dataset by Hendrycks et al. ("The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization") with 10,456 images spanning 62 of the 200 Tiny ImageNet dataset. It is a test set achieved by collecting images of joint classes of Tiny ImageNet and ImageNet. The resized images of size 64×64 contain art, cartoons, deviantart, graffiti, embroidery, graphics, origami, paintings, patterns, plastic objects, plush objects, sculptures, sketches, tattoos, toys, and video game renditions of ImageNet classes. For further information on ImageNet-R visit the original GitHub repository of ImageNet-R.

Tiny ImageNet-A is a subset of the Tiny ImageNet test set consisting of 3,374 images comprising real-world, unmodified, and naturally occurring examples that are misclassified by ResNet-18. The sampling process of Tiny ImageNet-A roughly follows the concept of ImageNet-A introduced by Hendrycks et al. ("Natural Adversarial Examples"). For further information on the sampling process visit the original paper.

Tiny-Imagenet is a dataset of 100,000 224x224 color images, each belonging to one of 200 classes.

torch-uncertainty/tiny-imagenet-200 dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by zhanghongyu

Contents

mini-ImageNet was proposed by Matching networks for one-shot learning for few-shot learning evaluation, in an attempt to have a dataset like ImageNet while requiring fewer resources. Similar to the statistics for CIFAR-100-LT with an imbalance factor of 100, we construct a long-tailed variant of mini-ImageNet that features all the 100 classes and an imbalanced training set with $N_1 = 500$ and $N_K = 5$ images. For evaluation, both the validation and test sets are balanced and contain 10K images, 100 samples for each of the 100 categories.

iny Imagenet has 200 Classes, each class has 500 traininig images, 50 Validation Images and 50 test images. Label Classes and Bounding Boxes are provided. More details can be found at https://tiny-imagenet.herokuapp.com/",

This challenge is part of Stanford Class CS 231N

Upload of the corrupted version of Tiny ImageNet (also known as ImageNet-200) with fixed frost-corrupted samples (see https://github.com/hendrycks/robustness/issues/60). This version can be downloaded on a server (for instance using TorchUncertainty) and is safer than the original mirror on berkeley connect (that may soon be deleted).

Original work by Dan Hendrycks & Thomas Dietterich under the Apache-2.0 license. If you consider this dataset useful, please cite:

@article{hendrycks2019robustness,
 title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
 author={Dan Hendrycks and Thomas Dietterich},
 journal={Proceedings of the International Conference on Learning Representations},
 year={2019}
}

The dataset used in the paper is CIFAR-10, Tiny ImageNet, and ImageNet.

Classification accuracy against PGD-10 attacks on different datasets.

Dataset

This dataset was created by Arghya Dutta

Contents

dnth/mini-imagenet dataset hosted on Hugging Face and contributed by the HF Datasets community

goddawg/tiny-imagenet-2k dataset hosted on Hugging Face and contributed by the HF Datasets community