660 datasets found

The dataset used in the paper is the ImageNet 2012 Large-Scale Visual Recognition Challenge dataset.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the OpenImages dataset to train their models.

The dataset used in the paper is the ImageNet dataset for image classification and the CACD dataset for face identification.

Two datasets for binary image classification: Jasmine and Basmati, and Arborio and Karacadag.

The dataset used in the paper is iNaturalist-2019, a large-scale image classification dataset.

Binary images are simple — two possible pixel-valued signals with a single channel. The simplicity of binary images has a significant advantage compared to colored and gray...

The MNIST handwritten digit database is a dataset of 60,000 8x8 grayscale images of handwritten digits.

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

The dataset used in the TextMesh paper, containing 35 text prompts for 3D mesh generation.

The GTSRB dataset contains over 50,000 images from German traffic signs that belong to 43 different classes.

VisDA2017 is a popular synthetic-to-real domain adaptation benchmark, which consists of 150k synthetic and 55k real images from 12 categories.

The dataset used in the paper is ILSVRC2012 (ImageNet 1K), a large-scale image classification dataset.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a subset of the validation dataset used for the ImageNet Large Scale Visual...

The MNIST and CIFAR10 datasets are used to evaluate the proposed Adversarial Training with Transferable Adversarial Examples (ATTA) method.

The ImageNet-21k and ImageNet-1k datasets are two large-scale image classification datasets.

Mixup is a commonly adopted data augmentation technique for image classification. Recent advances in mixup methods primarily focus on mixing based on saliency.

Class-Agnostic Counting (CAC) dataset, includes 6,135 images across 147 categories, divided into 3,659 training, 1,286 validation, and 1,190 test images.

The authors created a custom dataset for their experiment, consisting of 33,000 images of 320 possible object-image combinations, with 10 possible shapes, 8 possible colors, 2...

Mixup as a regularizer can surprisingly improve accuracy and out distribution robustness.

Tune it or don’t use it: Benchmarking data-efficient image classification.