24,167 datasets found

Improving zero and few-shot generalization in dialogue through instruction tuning

Human-generated scientific instructions for multimodal instruction tuning

The dataset used for testing the U-net architecture for automatic segmentation of LGE-MRI images.

The dataset used for training and testing the U-net architecture for automatic segmentation of LGE-MRI images.

The IBRNet dataset contains 10 scenes, each with 60 training views (with the object to be removed), 40 test views (without the object), and human-annotated object masks per view.

The SPIn-NeRF dataset contains 10 scenes, each with 60 training views (with the object to be removed), 40 test views (without the object), and human-annotated object masks per...

The Multi-PIE dataset consists of 754,200 images from 337 subjects with large variations in head pose, illumination, and facial expression.

A database for facial expression analysis

The Aff-Wild dataset is a large-scale in-the-wild dataset for affect recognition, containing actual videos with a wide range of content.

A multi-task learning framework for emotion recognition and facial image generation

The dataset used in the paper is CIFAR-100 and ImageNet.

UHD-LL dataset for low-light image enhancement

Multi-Component Image Dataset (MCID) is a test dataset containing a set number of components in a single image created by combining multiple images of the ImageNet dataset.

Kvasir-Instrument dataset consists of 590 annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc.

The MNIST Digit Swap Task dataset consists of 64 × 64 images of handwritten digits, where 0s are placed on the left and 1s are placed on the right, or vice versa.

The YOLOv3 dataset is a dataset for object detection and classification.

The proposed framework generates a training dataset in heterogeneous cluttered backgrounds for object detection and classification.

The dataset used in this paper is the Rosenbrock function and the Rastrigin function.

The dataset used in this paper is the wine dataset, iris dataset, digits dataset, boston dataset, diabete dataset, and linnerud dataset.

A binary imbalanced classification dataset with 32 × 32 color images of 10 classes of natural objects.