데이터셋 탐색

The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 different human activities and the poses are manually annotated with up to 16 body joints. Source: 2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning Image Source: http://human-pose.mpi-inf.mpg.de/

MPII Human Pose Dataset is a dataset for human pose estimation. It consists of around 25k images extracted from online videos. Each image contains one or more people, with over 40k people annotated in total. Among the 40k samples, ∼28k samples are for training and the remainder are for testing. Overall the dataset covers 410 human activities and each image is provided with an activity label. Images were extracted from a YouTube video and provided with preceding and following un-annotated frames. Source: Accelerating Large-Kernel Convolution Using Summed-Area Tables Image Source: http://human-pose.mpi-inf.mpg.de/

MPIIGaze is a dataset for appearance-based gaze estimation in the wild. It contains 213,659 images collected from 15 participants during natural everyday laptop use over more than three months. It has a large variability in appearance and illumination. Source: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild Image Source: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild

A dataset which provides detailed annotations for activity recognition. Source: Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data

The MPII Human Pose Descriptions dataset extends the widely-used MPII Human Pose Dataset with rich textual annotations. These annotations are generated by various state-of-the-art language models (LLMs) and include detailed descriptions of the activities being performed, the count of people present, and their specific poses. The dataset consists of the same image splits as provided in MMPose, with 14644 training samples and 2723 validation samples. Each image is accompanied by one or more pose descriptions generated by different LLMs. The descriptions are also accompanied by additional annotation information, including the activity type, people count, and pose keypoints, which are derived from the original MPII Human Pose Dataset annotations. By adding textual annotations to the existing human pose dataset, this extended version supports novel research in multi-modal learning, where both visual and textual cues can be explored.