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3s vs 7s MNIST problem
The 3s vs 7s MNIST problem is a classic dataset in machine learning. It consists of 28x28 grayscale images of handwritten digits, with 3s and 7s in the images. -
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: A deep neural network architecture for image classification and segmentation. -
BigEarthNet-MM
A large-scale benchmark archive for remote sensing image classification and retrieval. -
MoCo-SAS Dataset
The dataset used for the proposed MoCo-SAS framework, which consists of high-resolution Synthetic Aperture Sonar (SAS) data. -
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. -
Patch Camelyon
The Patch Camelyon dataset is a dataset of 1,000 images of 2 classes. -
Oxford Pets
The dataset used in the paper is a collection of trained networks and their corresponding datasets. -
TerraIncognita
The TerraIncognita dataset consists of 24,778 samples from four domains: painting, sketch, cartoon, and photo. -
Binary Image Classification with Small Samples
The dataset used for binary image classification with small samples -
FashionMNIST, CelebA, Cat vs Dog, and Imagenet
The dataset used in this paper is FashionMNIST, CelebA, Cat vs Dog, and Imagenet. -
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. -
MNIST and ResNet50
The MNIST and ResNet50 datasets are used to test the onnx-mlir compiler. -
TS-ENAS: Two-Stage Evolution for Cell-based Network Architecture Search
The proposed algorithm uses three benchmark classification problems from Fashion-MNIST, CIFAR10, and CIFAR100. -
CIFAR-10, CIFAR-100, and Tiny-Imagenet
The dataset used in the paper is CIFAR-10, CIFAR-100, and Tiny-Imagenet. -
Caltech Silhouettes dataset
The dataset used in the paper is a subset of the Caltech Silhouettes database, consisting of 11 images with 42 to 59 pixels in each class. -
DomainNet, ImageNet-R, ImageNet-B, and ImageNet-A
The dataset used in the paper is a classification dataset, specifically DomainNet, ImageNet-R, ImageNet-B, and ImageNet-A. -
CIFAR-10, CIFAR-100, TINY-IMAGENET, BASELINE, and PC-ANN
The dataset used in the paper is a classification dataset, specifically CIFAR-10, CIFAR-100, TINY-IMAGENET, BASELINE, and PC-ANN. -
Oxford 102 Category Flower Dataset
The Oxford 102 Category Flower dataset consists of 8,189 images with 102 categories of flowers which commonly occurs in the United Kingdom, and each category has 40 to 258 images.
