78 datasets found

The dataset used for image recognition using deep convolutional neural networks.

The ResNet-50 and ResNet-101 are used as the backbone image feature extractor.

The mini-ImageNet dataset is a subset of the ImageNet dataset, containing 60,000 images from 100 classes.

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

The PASCAL VOC dataset contains 20 classes, including person, animal, vehicle, and indoor, with 9,963 images containing 24,640 annotated objects.

Human Attribute Recognition (HAR) is a challenging task due to large variations of body gestures, external occlusions, lighting conditions, image resolutions and blurrinesses.

The VOC2012 dataset is a multi-label image recognition dataset. It contains 11,540 train-val images and 10,991 test images.

The MNIST dataset is a large dataset of handwritten digits, and the MNIST-rot dataset is a rotated version of the MNIST dataset.

Alpha-CLIP is an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs.

The dataset used in the paper is a large-scale dataset for unsupervised learning on diffusion-generated images.

The ship discrimination dataset contains SAR images of ships and non-ships

The MSTAR dataset contains SAR images of ten classes of ground vehicles

The proposed GNN model for SAR automatic target recognition

The dataset used in this paper is the CIFAR10 dataset, which contains 60,000 32x32 color images in 10 classes, with 6,000 images per class.

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 dataset used in this paper is the VGG Network E, a deep convolutional neural network for image recognition.

The dataset consists of binary images of sprites with variations in the shape (oval, square, and heart) and four geometric factors: scale (6 variation modes), rotation (40), and...

Deep Image: Scaling up image recognition