15 datasets found

A dataset of images of birds.

Caltech-UCSD Birds (CUB 200-2007) and extended version CUB 200-2011 image collections tagged with keypoints, bounding boxes, coarse segmentation, and attribute labels.

The Caltech-UCSD Birds (CUB) dataset is a fine-grained visual classification dataset containing 200 classes of birds.

The CUB dataset is a large collection of images of birds, each image is a 299x299 RGB image, and there are 11,778 training images and 5,994 testing images.

A dataset of 11,788 images of birds, each annotated with 36 attributes.

The CUB dataset comprises 200 bird species totaling 11,788 image samples, of which 50 categories are planned as unseen classes. The SUN dataset has a sample of 717 different...

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

The CUB dataset is a collection of images of landbirds and waterbirds from the CUB dataset, combined with images from Places dataset as background.

The Caltech-UCSD Birds dataset contains images of birds from 200 classes.

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

Fine-grained visual classification (FGVC) is a challenging computer vision problem, where the task is to au- tomatically recognise objects from subordinate categories.

Caltech-UCSD Birds (CUB) dataset is a dataset of bird images.

The Birds-to-Words dataset contains 15,931 images (12,770 training and 3,151 testing) tagged with descriptions of fine-grained differences between pairwise bird images.

The dataset used in the paper is CUB200-2011, a fine-grained image classification dataset.

CUB-200 is a fine-grained image classification dataset containing high resolution images of 200 different bird species.