660 datasets found

The VGG16-D dataset is a large dataset of images, used for training and testing neural networks.

The dataset used in the paper is the Cars Overhead With Context (COWC) dataset, which contains images of cars in overhead imagery.

Long-tailed classification has been extensively studied in recent years due to its importance in real-world applications with heavily imbalanced data distribution.

The Street View House Numbers (SVHN) dataset used consist of 32x32 10,000 labelled image pool, 30,000 unlabelled pool and 26,032 testing pool.

The Open Images dataset is a large-scale image dataset with a wide range of images, including but not limited to, street scenes, indoor scenes, and outdoor scenes.

Four remote sensing datasets were used for classification with both small and large training samples.

The BDD100k dataset is a large-scale driving dataset containing 100,000 images of various weather conditions, including clear, rainy, foggy, and night-time.

The ESD dataset offers finetuned weights for the 'car' and 'French-horn' classes.

The French-horn dataset comprises merely 10 identical prompts with different evaluation seeds.

The Inappropriate Image Prompts (I2P) dataset encompasses various uncomfortable and inappropriate prompts (including hate, harassment, violence, self-harm, nudity contents,...

The dataset used in the paper is the Pascal VOC 2012 dataset, which is a benchmark for instance segmentation. The dataset consists of 1464 images with 20 class categories and...

The dataset used in the paper is ImageNet-1K, ADE20K, and COCO 2017.

ImageNet-Compatible Dataset is a dataset of 1000 color images with 1000 class labels.

The VOC07 dataset is used for evaluating the performance of object detection and image classification methods.

The Caltech dataset is a dataset of images used to evaluate the performance of the ODIN system.

The Indian Food dataset is a dataset of images used to evaluate the performance of the ODIN system.

The Microsoft Common Objects in Context (COCO) dataset is a benchmark for object detection, segmentation, and image classification.

The Pascal Visual Object Classes (VOC) dataset is a benchmark for object detection and image classification.

The dataset used in the paper is CIFAR-10, CIFAR-100, Tiny-ImageNet, and ResNet-50.

The dataset used in this paper for image classification and object detection tasks.