12 datasets found

The Caltech-USCD Birds-200-2011 dataset contains 30,607 images of 200 bird species.

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

CUB Bird dataset is a dataset of 500 images of 200 bird species, FGVC Aircraft dataset is a dataset of 100 aircraft models, DTD dataset is a dataset of 47 textures, ADE20K...

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.

Dataset bias is a significant problem in training fair classifiers. When attributes unrelated to classification exhibit strong biases towards certain classes, classifiers...

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

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 CUB 200-2011 dataset contains 200 classes of bird species in 11,788 images with approximately 30 examples per class in the training set.

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.

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