21 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.

Selective search is a method for object detection.

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 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 Willow Object Class dataset comprises 304 images gathered from Caltech-256 (Griffin et al., 2007) and Pascal VOC 2007 (Everingham et al., 2007).

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 Small-NORB dataset is a dataset of 4,000 images of 6 classes, each class containing 60 images of size 16x16.

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.

VisDA-2017 is a simulation-to-real dataset with two extremely distinct domains: Synthetic renderings of 3D models and Real collected from photo-realistic or real-image datasets.

The dataset used in this paper is a neural network, and the authors used it to test the performance of their lookahead pruning method.

The dataset used for training and validation of the proposed approach to combine semantic segmentation and dense outlier detection.

A benchmark for low-shot recognition was proposed by Hariharan & Girshick (2017) and consists of a representation learning phase without access to the low-shot classes and a...