15 datasets found

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-200-2011 dataset.

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.

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.

Deep metric learning (DML) methods typically leverage solely class labels to keep positive samples far away from negative ones. However, this type of method normally ignores the...

The NA-Birds dataset is larger than CUB200-2011, has 555 bird species, 23,929 training images and 24,633 test images.

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

Caltech-UCSD Birds (CUB) 200-2011 is a frequently used benchmark for unsupervised image segmentation. It consists of 11,788 images from 200 bird species.