564 datasets found

This paper proposes a convolutional neural network hardware accelerator based on field programmable logic gate array (FPGA).

BigEarthNet is a large-scale Sentinel-2 dataset collected from a total of 125 Sentinel-2 tiles covering areas of 10 countries in Europe. The dataset was prepared with data from...

A custom dataset of 200 representative design diagrams images were collected, cleansed, accurately annotated, trained and tested using the proposed model.

SynthCity: A large scale synthetic point cloud dataset for pre-training deep learning models, enabling generalisation and expansion of their usage to real-world data.

The dataset used in the paper is the Stable Diffusion safety filter dataset, which contains images that are generated using the Stable Diffusion model and are classified as safe...

The proposed FS-Net detection scheme is a DNN-aided symbol-detection algorithm for MIMO systems. Unlike the prior DetNet and ScNet schemes, the input vector of the FS-Net is...

A novel food image dataset with volume and mass records of foods, and a deep learning method for food detection, to make a complete calorie estimation.

Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance...

The ImageNet dataset is a large-scale image database that contains over 14 million images, each labeled with one of 21,841 categories.

The dataset used in this paper is CINIC-10, a dataset compiled by combining downsampled images from the Imagenet database with CIFAR-10.

A dataset of sagittally-sliced lumbar T1 and T2 scans from 6 different international clinical spinal imaging centres.

Two datasets used to train the SpineNetV2 pipeline: Oxford Whole Spine (OWS) and Genodisc.

The Deep High-Resolution Representation Learning for Visual Recognition dataset.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors tested their model on various signal reconstruction tasks: 1D sinusoidal...

CLIC 2019 Professional dataset

Variable rate deep image compression with modulated autoencoder

Moving MNIST is a benchmark data set for video recognition. There are 10,000 samples including 8,000 for training and 2,000 for test. Each sample consists of 20 sequential gray...

A dataset of images of holy places (Kaaba, Zamzam, Maqam Ibrahim) for training a deep learning model.

Latent factor analysis via dynamical systems (LFADS) is an RNN-based variational sequential autoencoder that achieves state-of-the-art performance in denoising high-dimensional...

The dataset used for the experiments of the paper "CORE-PERIPHERY PRINCIPLE GUIDED REDISIGN OF SELF-ATTENTION IN TRANSFORMERS"