Open Images Dataset
The Open Images Dataset is a large-scale dataset for object detection and image classification, featuring millions of labeled images across thousands of categories.
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The Open Images Dataset is a large-scale dataset for object detection and image classification, featuring millions of labeled images across thousands of categories.
Fashion-MNIST is a dataset used for training machine learning models, consisting of images of clothing items to aid in image classification tasks.
VoxCeleb is a large-scale speaker recognition dataset designed for research in speaker verification and recognition tasks.
The MRPC is a dataset developed by Microsoft Research for paraphrase identification tasks, consisting of pairs of sentences labeled for semantic similarity.
UCF101 is a widely used dataset for action recognition in videos, containing 13,320 clips across 101 action categories.
BoolQ is a dataset designed for evaluating natural language understanding and question-answering systems, featuring yes/no questions derived from Wikipedia articles.
CoQA is a dataset designed to facilitate the development of conversational question answering systems. It allows computers to engage in human-like dialogue by answering questions based on provided context.
Open X-Embodiment is a comprehensive robotics dataset designed for advancing research in embodied AI and robotics applications.
CIFAR-100 is a dataset widely used in machine learning and computer vision, consisting of 60,000 images across 100 classes.
CIFAR-10 is a widely used dataset in machine learning for image classification, consisting of 60,000 32×32 color images in 10 different classes.