25 datasets found

The COIL100 dataset is a benchmark dataset for object recognition and image classification.

The VOC2007 dataset is a benchmark dataset for object recognition and image classification.

Spatial pattern templates for recognition of objects with regular structure.

The Sprites dataset has 672 synthetically rendered animated characters (sprites). The dataset is split into a training set with 500, a validation set with 72, and a test set...

Fine-Grained Visual Classification (FGVC) is an important computer vision prob-lem that involves small diversity within the different classes, and often requires expert...

The dataset used in the experiment consists of 50 images equally distributed between five classes: aircraft, bird, bicycle, boat, and dog. Each class has 5 true positive images...

The Stanford Online Products (SOP) dataset contains 120,053 product images covering 22,634 categories. The training set is composed of 59,551 images of the first 11,318...

Selective search is a method for object detection.

The dataset used in the paper is a set of images, and the authors used it to train and test their ladder network model.

The CIFAR-100 dataset consists of 100 classes of 32 × 32 RGB images with 60,000 training and 10,000 testing examples.

The Caltech-UCSD Birds-200-2011 Dataset consists of 11,169 bird images from 200 categories and each category has 60 images averagely.

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

The Places dataset is a large-scale dataset for scene recognition, containing 1 million images from 365 categories.

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 the paper is a collection of images of objects with varying viewpoints, used for training and testing the proposed RRB framework.

AWA2 is an animal dataset containing 37,322 images from 50 classes, with 85 attributes provided by experts to describe the semantic feature of each class.

The dataset used in the paper is CIFAR10-DVS, a dataset of 10,000 event streams of 128x128 images.

The MiniImagenet dataset is a benchmark for few-shot learning, consisting of 60,000 images from 21 classes, each with 300 images.

Multi-label image recognition is a practical and challenging task compared to single-label image classification.

The Caltech-256 object category dataset is used for the feature extraction step, and the Omniglot dataset is used for the evaluation.