361 datasets found

The dataset used in this paper is a simulated dataset with 200 variables and 50 observations. The variables are generated from a multivariate normal distribution with a...

The dataset used in this paper for testing the 3D-RecGAN++ model. It contains 1.5k SV and 2.5k CV testing datasets for each of the 6 categories.

A dataset for segment anything model (SAM) to evaluate its performance.

LVIS: A dataset for large vocabulary instance segmentation.

Segment anything model (SAM) addresses two practical yet challenging segmentation tasks: segment anything (SegAny), which utilizes a certain point to predict the mask for a...

The authors' own dataset is used for object detection in thermal images.

A fine-grained vehicle detection dataset with 3,640 instances and 1,210 images, which provides fine-grained information of car categories.

DOTA dataset is a large-scale dataset for object detection in aerial images including 2806 aerial images with each image ranging from 800 × 800 to 4000 × 4000 pixels collected...

Video object segmentation is a fundamental task in many important areas such as autonomous driving, robotic manipulation, video surveillance, and video editing.

The VIPSeg dataset contains videos with annotations for video panoptic segmentation.

The LVIS-92i dataset contains 92 images with 92 object categories.

The PACO-Part dataset contains 59 images with 59 object categories.

The PerSeg dataset contains 40 images with 40 object categories.

The Caltech-UCSD-Birds-200-2011 dataset contains images of 200 bird species.

The dataset used for pothole detection using deep learning

A dataset of images with pothole annotations from various sources, including Google Earth Pro, AUTOPILOT videos, and GoPro camera images.

The DAVIS dataset is a widely used dataset for video-related tasks, consisting of approximately 2000 frames from 26 human-centric scenarios.

The dataset used in the paper for image classification, object detection, and face verification tasks.

A survey of the foundations, selected improvements, and some current applications of Deep Convolutional Neural Networks (CNNs).

A dataset of textual entailment tasks, used for evaluating the ability of language models to understand relationships between texts.