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UT Zappos50K dataset
UT Zappos50K dataset -
Split CIFAR100
A variant of CIFAR-100 dataset, where the original dataset is split into 20 disjoint tasks, each consisting of 2,500 samples from 5 classes. -
FashionMNIST and CIFAR-10
The dataset used in the paper is FashionMNIST and CIFAR-10, which are commonly used datasets for image classification tasks. -
ImageNet and SST2 datasets
The dataset used in this study for image and text classification tasks. -
Occupancy Detection in Vehicles Using Fisher Vector Image Representation
A dataset of 3000 images collected on a public roadway for front seat vehicle occupancy detection. -
CIFAR-100 and AGNews
Two datasets used for multi-task learning, CIFAR-100 and AGNews. -
ImageNet Noise
The dataset used in the paper is the ImageNet noise dataset, which contains 60,000 32x32 color images with random labels. -
ResNet-VAE
The dataset used in this paper is a large-scale neural network model, specifically a ResNet-VAE model, trained on the CIFAR-10 dataset. -
MNIST, Fashion MNIST, and CIFAR-10
The dataset used in the paper is MNIST, Fashion MNIST, and CIFAR-10. -
MOON dataset
The MOON dataset is used to test the proposed TEMP-based spiking neural network. -
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. -
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. -
MNIST and ResNet50
The MNIST and ResNet50 datasets are used to test the onnx-mlir compiler. -
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.
