492 datasets found

Tags: Image Classification

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  • MNIST and CIFAR-10 datasets

    The MNIST and CIFAR-10 datasets are used to test the theory suggesting the existence of many saddle points in high-dimensional functions.

  • AlexNet

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

  • Caltech-UCSD-Birds-200-2011

    The Caltech-UCSD-Birds-200-2011 dataset contains images of 200 bird species.

  • CIFAR10, CIFAR100, and SVHN

    CIFAR10, CIFAR100, and SVHN datasets

  • DeiT-Base

    The dataset used in the paper is also used for training and evaluation of the proposed method.

  • MobileNet-V1

    The dataset used in the paper is also used for training and evaluation of the proposed method.

  • Salinas Valley

    Three famous HSI data sets are used to demonstrate the reliability of the proposed method, which are University of Pavia (PU), Salinas (SA), and Indian Pines (IP).

  • MNIST and USPS

    The MNIST and USPS datasets are used for binary classification tasks.

  • CUB 200-2011

    The CUB 200-2011 dataset contains 200 classes of bird species in 11,788 images with approximately 30 examples per class in the training set.

  • CIFAR-10 and STL-10

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

  • ResNet-18

    Training neural networks on image datasets generally require extensive experimentation to find the optimal learning rate regime.

  • ConvNet

    The dataset used in this paper is a convolutional neural network problem with 60,000 training examples and 10,000 testing examples.

  • CIFAR-100 and ImageNet

    The dataset used in the paper is CIFAR-100 and ImageNet.

  • Cifar-10

    A binary imbalanced classification dataset with 32 × 32 color images of 10 classes of natural objects.

  • Fashion-Mnist

    A binary imbalanced classification dataset with 28 × 28 grayscale images of 10 classes corresponding to fashion products.

  • Mnist

    A binary imbalanced classification dataset with 28 × 28 grayscale images of 10 classes corresponding to digits from 0 to 9.

  • ImageNet-Dogs

    The dataset used in the paper for image classification, object detection, and face verification tasks.

  • CIFAR10 and CIFAR100 datasets

    The CIFAR10 and CIFAR100 datasets are used to evaluate the proposed randomized defense method.

  • CIFAR10 and ImageNet Datasets

    CIFAR10 and ImageNet datasets are used as the original task for the pre-trained models.

  • TDT4173 - Method Paper

    A survey of the foundations, selected improvements, and some current applications of Deep Convolutional Neural Networks (CNNs).
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