492 datasets found

CIFAR-10-C is a dataset of 60,000 32x32 color images in 10 classes, with 6,000 images per class, and 10% of the images are corrupted.

The dataset used in the paper is Dogs vs. Cats, a binary classification dataset containing 3,000 dog and cat images.

This paper uses the UCF-101, Event Recognition in Aerial videos (ERA), and Biased Action Recognition (BAR) datasets for image classification tasks.

The dataset used in the paper is the CIFAR-100-C dataset, which applies common corruptions such as changes in contrast or blurring to images from CIFAR-100.

The CIFAR-10 dataset is a popular benchmark for image classification tasks.

CIFAR-10 and CIFAR-100 are standard vision datasets with 50,000 training images across 10 and 100 classes, respectively.

MIT-67 is a dataset of 67 indoor scenes, CUB-2011 is a dataset of 200 bird species, Caltech-101 is a dataset of 101 objects, and DTD is a dataset of 47 textures.

We explore the problem of selectively forgetting categories from trained CNN classification models in federated learning (FL).

Image bert pre-training with online tokenizer.

The MS COCO 2014 Dataset contains images of 91 object categories, which contains 82783 training images, 40504 validation images and 40775 testing images.

The SDI dataset is a collection of 8,000 images of defects in industrial products.

The Oxford Flower dataset is a collection of 2,000 images of flowers in 80 classes.

The CUB Bird dataset is a collection of 4,521 images of birds in 200 classes.

The dataset used in the paper is the ImageNet validation set, a subset of the ImageNet dataset.

The dataset used in the paper is the YFCC-100M dataset, a large-scale image dataset.

The dataset used in the paper for diffusion model detection, containing synthetic images and real images.

The dataset used in the paper is CIFAR-10 and Fashion-MNIST.

The Stanford Cars dataset is a dataset of images of cars, with 196 categories and approximately 16,000 images. The authors created a synthetic dataset by adding occlusions of...

The dataset used in this paper is the FashionMNIST dataset, which consists of 60,000 images of clothing items from 10 different classes.

The dataset used in this paper is the CIFAR10 dataset, which contains 60,000 32x32 color images in 10 classes, with 6,000 images per class.