66 datasets found

The dataset used in this study for image and text classification tasks.

The ImageNet ILSVRC 2012 validation dataset is used to evaluate the proposed approach.

The dataset used in this paper is not explicitly described. However, it is mentioned that the authors used datasets such as ImageNet and YouTube-8M.

The dataset used in the WaveAttack paper, which consists of four classical benchmark datasets: CIFAR-10, CIFAR-100, GTSRB, and a subset of ImageNet.

The dataset used in the paper is ImageNet 642, a large-scale image classification dataset.

The dataset used in the paper is ImageNet 322, a large-scale image classification dataset.

The dataset used in the paper is the ImageNet 2012 dataset.

The Imagenette dataset used in the paper for class density and dataset quality in high-dimensional, unstructured data.

ImageNet Subsets

CelebA-HQ, ImageNet

The dataset used in this paper is the MobileNetV1, MobileNetV2 and MNasNet models for ImageNet classification.

The dataset used for ImageNet recognition, ADE20K semantic segmentation, and COCO panoptic segmentation.

Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations

Large-scale text-to-image diffusion models can be fine-tuned to produce class-conditional models with SOTA FID and Inception Score on ImageNet.

The dataset used in the paper is ImageNet-1k and ImageNet-100, which are large-scale image classification datasets.

ImageNet trained PyTorch models are evaluated under various simple image transformations.

The proposed DualConv is used to replace the standard convolution in VGG-16 and ResNet-50 to perform image classification experiments on CIFAR-10, CIFAR-100, and ImageNet datasets.

The dataset used in the paper is Tiny-ImageNet-200, which consists of 100k training, 10k validation, and 10k test images of dimensions 64x64x3.

ImageNet classification dataset, COCO dataset

The dataset used in the paper for attention-oriented data analysis and attention-based adversarial defense.