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Fifteen Natural Scene Categories
The Fifteen Natural Scene Categories database is used for bag-of-features based image classification problem. -
MIDOG21 Dataset
A dataset used for testing the proposed Deep Feature Learning method for histopathology image classification. -
In-House Dataset
A dataset used for training and testing the proposed Deep Feature Learning method for histopathology image classification. -
Evaluation dataset for histopathology image classification
A dataset composed of 35334 breast histopathology images at zoom x5 (1.76 µm per pixel) distributed amongst 23 imbalanced classes, which include both common tumor and benign... -
MNIST data set
The MNIST data set is a dataset used for testing the performance of the DisDF. -
ResNet-56 Dataset
The dataset used in the paper for hyper-parameter tuning using transient cloud resources. -
BigEarthNet-MM
A large-scale benchmark archive for remote sensing image classification and retrieval. -
Caltech-UCSD 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. -
TerraIncognita
The TerraIncognita dataset consists of 24,778 samples from four domains: painting, sketch, cartoon, and photo. -
Multi-Fashion+Multi-MNIST
The dataset used in the paper is a multi-task learning dataset, where the goal is to learn a shared feature extractor and a task-specific predictor for multiple tasks. -
Playing cards
A synthetic image dataset for evaluating Concept Bottleneck Models (CBMs) with perfect concept annotations. -
Lightweight Residual Network for The Classification of Thyroid Nodules
99 images of benign and malignant thyroid nodules for classification -
Sequence Counting and MNIST Classification
The proposed method is evaluated on a toy example problem of sequence counting followed by MNIST classification problem. -
Cars dataset
The Cars dataset is a multi-object multi-camera network application. -
MNIST digits
The MNIST digits dataset used in the one-shot learning experiments.