28 datasets found

Image Recognition FeedbackLog

The DenseNet dataset is a dataset for image recognition on handwritten digits composed of 60,000 training data and 10,000 test images.

The dataset consists of 60,000 images of objects in 200 categories, with 300 images per category.

The iNaturalist dataset is a crowdsourced compendium of living organisms, with fine- and coarse-grained species distinctions, a heavy-tailed class size distribution, and...

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset is a large-scale image classification dataset containing over 14 million images from 21,841 categories.

Conformer is a dual network structure that combines CNN-based local features with transformer-based global representations for enhanced representation learning.

This dataset is used to evaluate the performance of a Convolutional Neural Network (CNN) on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC2012).

This dataset is used to evaluate the performance of a Convolutional Neural Network (CNN) on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC2012).

The MIT Indoor Scene Recognition dataset contains 67 categories of indoor scenes.

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

A dataset of ImageNet images with subtle 3D perspective changes that can break ImageNet-trained classification networks.

A dataset of complex image data with a fixed, known distribution, generated using a computer graphics pipeline.

The dataset used in the paper is Places, a large dataset of 400k pairs of images from the Places 205 dataset and corresponding spoken audio captions.

Training neural networks on image datasets generally require extensive experimentation to find the optimal learning rate regime.

The dataset used in the paper is the Imagenet dataset, which is a large annotated dataset of images used for image classification tasks.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a subset of the validation dataset used for the ImageNet Large Scale Visual...

Multi-label image recognition is a practical and challenging task compared to single-label image classification.

The paper presents a method for reading digits in natural images using unsupervised feature learning.

The MNIST database of handwritten digits is a popular benchmark data set for classification algorithms.

Object recognition is arguably the most important problem at the heart of computer vision. Recently, Barbu et al. introduced a dataset called ObjectNet which includes objects in...