19,997 machine learning datasets
19,997 dataset results
LDCT-and-Projection-data 医学去噪数据集
LSA-T is the first continuous Argentinian Sign Language (LSA) dataset. It contains 14,880 sentence level videos of LSA extracted from the CN Sordos YouTube channel with labels and keypoints annotations for each signer. Videos are in 30 FPS full HD (1920x1080).
To construct our multilingual dataset - mBBC - we gathered news articles from various BBC news websites in 43 different languages. This selection was based on the fact that BBC broadcasts news in these 43 languages, providing a global coverage across continents, and spanning a diverse range of language families, scripts, resource-levels, and word order ensuring a comprehensive representation of linguistic diversity. We collected data from various language families such as Indo-European, Sino-Tibetan, Niger-Congo, Austronesian, Dravidian, and more, encompassing several scripts like Latin, Cyrillic, Arabic, Devanagari, Chinese characters, and others. This extensive representation facilitates a comprehensive evaluation of multilingual language models across different linguistic contexts. Moreover, the dataset includes both high-resource languages like English, Spanish, and French, benefiting from extensive linguistic resources, as well as low-resource languages such as Somali, Burmese, an
InstructCoder is the first dataset designed to adapt LLMs for general code editing. It consists of over 100k instruction-input-output triplets and covers multiple distinct code editing scenarios, generated by ChatGPT. LLaMA-33B finetuned on InstructCoder performs on par with ChatGPT on a real-world test set derived from GitHub commits.
The Chess Recognition Dataset (ChessReD) comprises a diverse collection of images of chess formations captured using smartphone cameras; a sensor choice made to ensure real-world applicability. The dataset is accompanied by detailed annotations providing information about the chess pieces formation in the images. Therefore, the number of annotations for each image depends on the number of chess pieces depicted in it. There are 12 category ids in total (i.e., 6 piece types per colour) and the chessboard coordinates are in the form of algebraic notation strings (e.g., "a8").
The Chess Recognition Dataset 2K (ChessReD2K) comprises a diverse collection of images of chess formations captured using smartphone cameras; a sensor choice made to ensure real-world applicability. The dataset is accompanied by detailed annotations providing information about the chess pieces formation in the images, bounding-boxes, and chessboard corner annotations. The number of annotations for each image depends on the number of chess pieces depicted in it. There are 12 category ids in total (i.e., 6 piece types per colour) and the chessboard coordinates are in the form of algebraic notation strings (e.g., "a8"). The corners are annotated based on their location on the chessboard (e.g., "bottom-left") with respect to the white player's view. This discrimination between these different types of corners provides information about the orientation of the chessboard that can be leveraged to determine the image's perspective and viewing angle.
The dataset contains three subsets:
an image cover dataset in short video recommendation
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The Creative Visual Storytelling Anthology is a collection of 100 author responses to an improved creative visual storytelling exercise over a sequence of three images. Each item contains four facet entries, corresponding to Entity, Scene, Narrative, and Title.
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Card is a dataset of playing card images, which consists of 8,029 images with two clusterings, i.e., rank (Ace, King, Queen, etc.) and suits (clubs, diamonds, hearts, spades).
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We study human pose estimation in extremely low-light images. This task is challenging due to the difficulty of collecting real low-light images with accurate labels, and severely corrupted inputs that degrade prediction quality significantly. To address the first issue, we develop a dedicated camera system and build a new dataset of real low-light images with accurate pose labels. Thanks to our camera system, each low-light image in our dataset is coupled with an aligned well-lit image, which enables accurate pose labeling and is used as privileged information during training. We also propose a new model and a new training strategy that fully exploit the privileged information to learn representation insensitive to lighting conditions. Our method demonstrates outstanding performance on real extremely low-light images, and extensive analyses validate that both of our model and dataset contribute to the success.
Question Answering Sirah Nabawiyah (QASiNa) Dataset is a reading comprehension dataset consists of QA from Sirah Nabawiyah literature in Indonesian Language
75k photos of windows + 21k synthetic renders of building windows.
Overview of the scoping review paper corpus, sorted by their diferent intent types, categories, and subcategories. Note: Papers (77) may include multiple unique intents (172) and can therefore appear in multiple categories and subcategories.
The dataset identifies the shortcomings of existing benchmarks in evaluating the problem of compositional generalization, which underscores the need for the development of datasets tailored to assess compositional generalization in open intent detection tasks.
The dataset identifies the shortcomings of existing benchmarks in evaluating the problem of compositional generalization, which underscores the need for the development of datasets tailored to assess compositional generalization in open intent detection tasks.
The dataset identifies the shortcomings of existing benchmarks in evaluating the problem of compositional generalization, which underscores the need for the development of datasets tailored to assess compositional generalization in open intent detection tasks.