The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('cifar10', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/cifar10-3.0.2.png" alt="Visualization" width="500px">

This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs).

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('cifar100', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/cifar100-3.0.2.png" alt="Visualization" width="500px">

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

wasifis/cifar-10-100 dataset hosted on Hugging Face and contributed by the HF Datasets community

The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.

Here are the classes in the dataset, as well as 10 random images from each: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck

The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks.

The dataset used in the paper is a benchmark dataset for diffusion models, specifically denoising diffusion probabilistic models (DDPM). The dataset consists of images from CIFAR-10, CIFAR-100, and MNIST.

The dataset used in the paper is CIFAR-10 and CIFAR-100, and ILSVRC-12.

Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD Detection:

This paper presents a novel evaluation framework for Out-of-Distribution (OOD) detection that aims to assess the performance of machine learning models in more realistic settings. We observed that the real-world requirements for testing OOD detection methods are not satisfied by the current testing protocols. They usually encourage methods to have a strong bias towards a low level of diversity in normal data. To address this limitation, we propose new OOD test datasets (CIFAR-10-R, CIFAR-100-R, and ImageNet-30-R) that can allow researchers to benchmark OOD detection performance under realistic distribution shifts. Additionally, we introduce a Generalizability Score (GS) to measure the generalization ability of a model during OOD detection. Our experiments demonstrate that improving the performance on existing benchmark datasets does not necessarily improve the usability of OOD detection models in real-world scenarios. While leveraging deep pre-trained features has been identified as a promising avenue for OOD detection research, our experiments show that state-of-the-art pre-trained models tested on our proposed datasets suffer a significant drop in performance. To address this issue, we propose a post-processing stage for adapting pre-trained features under these distribution shifts before calculating the OOD scores, which significantly enhances the performance of state-of-the-art pre-trained models on our benchmarks.

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.

Classification performance of the Wide ResNet-32 architectures on the CIFAR-10 and CIFAR-100 datasets.

The dataset used in the paper is Cifar-10 and Cifar-100, which are commonly used datasets for image classification tasks.

Similar Datasets:

CIFAR-10 Python (in CSV): LINK

Context

The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 50000 training images and 10000 test images. The meta file contains the label names of each class and superclass.

Content

Here is the list of the 100 classes in the CIFAR-100:

Classes: 1-5) beaver, dolphin, otter, seal, whale 6-10) aquarium fish, flatfish, ray, shark, trout 11-15) orchids, poppies, roses, sunflowers, tulips 16-20) bottles, bowls, cans, cups, plates 21-25) apples, mushrooms, oranges, pears, sweet peppers 26-30) clock, computer keyboard, lamp, telephone, television 31-35) bed, chair, couch, table, wardrobe 36-40) bee, beetle, butterfly, caterpillar, cockroach 41-45) bear, leopard, lion, tiger, wolf 46-50) bridge, castle, house, road, skyscraper 51-55) cloud, forest, mountain, plain, sea 56-60) camel, cattle, chimpanzee, elephant, kangaroo 61-65) fox, porcupine, possum, raccoon, skunk 66-70) crab, lobster, snail, spider, worm 71-75) baby, boy, girl, man, woman 76-80) crocodile, dinosaur, lizard, snake, turtle 81-85) hamster, mouse, rabbit, shrew, squirrel 86-90) maple, oak, palm, pine, willow 91-95) bicycle, bus, motorcycle, pickup truck, train 96-100) lawn-mower, rocket, streetcar, tank, tractor

and the list of the 20 superclasses: 1) aquatic mammals (classes 1-5) 2) fish (classes 6-10) 3) flowers (classes 11-15) 4) food containers (classes 16-20) 5) fruit and vegetables (classes 21-25) 6) household electrical devices (classes 26-30) 7) household furniture (classes 31-35) 8) insects (classes 36-40) 9) large carnivores (classes 41-45) 10) large man-made outdoor things (classes 46-50) 11) large natural outdoor scenes (classes 51-55) 12) large omnivores and herbivores (classes 56-60) 13) medium-sized mammals (classes 61-65) 14) non-insect invertebrates (classes 66-70) 15) people (classes 71-75) 16) reptiles (classes 76-80) 17) small mammals (classes 81-85) 18) trees (classes 86-90) 19) vehicles 1 (classes 91-95) 20) vehicles 2 (classes 96-100)

Acknowledgements

• Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009. Link

How to load the data (Python)

The function used to open each file: def unpickle(file): import pickle with open(file, 'rb') as fo: dict = pickle.load(fo, encoding='bytes') return dict

Example of how to read the metadata and the superclasses: metadata_path = './cifar-100-python/meta' # change this path`\ metadata = unpickle(metadata_path) superclass_dict = dict(list(enumerate(metadata[b'coarse_label_names'])))

How to load the training and test sets (using superclasses): ``` data_pre_path = './cifar-100-python/' # change this path

File paths

data_train_path = data_pre_path + 'train' data_test_path = data_pre_path + 'test'

Read dictionary

data_train_dict = unpickle(data_train_path) data_test_dict = unpickle(data_test_path)

Get data (change the coarse_labels if you want to use the 100 classes)

data_train = data_train_dict[b'data'] label_train = np.array(data_train_dict[b'coarse_labels']) data_test = data_test_dict[b'data'] label_test = np.array(data_test_dict[b'coarse_labels']) ```

The CIFAR-100 dataset This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). Here is the list of classes in the CIFAR-100:

Dataset Card for "cifar-10-100-longtail"

More Information needed

The CIFAR-10 and CIFAR-100 datasets are used to evaluate the performance of the commentaries curriculum.

Datasets:

• Asirra
• CIFAR-10
• CIFAR-100
• GTSRB
• HASYv2
• MNIST
• STL-10
• SVHN

The dataset used in the paper is CIFAR-10 and CIFAR-100, as well as SVHN.

Top-1 accuracy of student network with VGG8 on CIFAR-100 test set.

Different Artificial Neural Networks (saved weights), some only pre-trained either on rwave-1024 or rwave-4096 or FractalDB1000 datasets; some fine-tuned or trained from scratch (pt_none_ft... or pt_ft... or ...scratch...) on CIFAR10/100 or ImageNet1k. Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development - see https://github.com/BennyCa/ReWaRD for filter visualization and further fine-tuning possibilities Pre-training and fine-tuning was conducted using the codebase https://github.com/hirokatsukataoka16/FractalDB-Pretrained-ResNet-PyTorch

This work presents two new benchmark datasets (CIFAR-10N, CIFAR-100N), equipping the training dataset of CIFAR-10 and CIFAR-100 with human-annotated real-world noisy labels that we collect from Amazon Mechanical Turk.