19,997 machine learning datasets
19,997 dataset results
The Fabrics Dataset consists of about 2000 samples of garments and fabrics. A small patch of each surface has been captured under 4 different illumination conditions using a custom made, portable photometric stereo sensor. All images have been acquired "in the field" (at clothes shops) and the dataset reflects the distribution of fabrics in real world, hence it is not balanced. The majority of clothes are made of specific fabrics, such as cotton and polyester, while some other fabrics, such as silk and linen, are more rare. Also, a large number of clothes are not composed of a single fabric but two or more fabrics are used to give the garment the desired properties (blended fabrics). For every garment there is information (attributes) about its material composition from the manufacturer label and its type (pants, shirt, skirt etc.).
The Large Scale Facial Model (LSFM) is a 3D statistical model of facial shape built from nearly 10,000 individuals.
The DR HAGIS database has been created to aid the development of vessel extraction algorithms suitable for retinal screening programmes. Researchers are encouraged to test their segmentation algorithms using this database.
The VICAVR database is a set of retinal images used for the computation of the A/V Ratio. The database currently includes 58 images. The images have been acquired with a TopCon non-mydriatic camera NW-100 model and are optic disc centered with a resolution of 768x584. The database includes the caliber of the vessels measured at different radii from the optic disc as well as the vessel type (artery/vein) labelled by three experts.
The database consists of 89 colour fundus images of which 84 contain at least mild non-proliferative signs (Microaneurysms) of the diabetic retinopathy, and 5 are considered as normal which do not contain any signs of the diabetic retinopathy according to all experts who participated in the evaluation. Images were captured using the same 50 degree field-of-view digital fundus camera with varying imaging settings. The data correspond to a good (not necessarily typical) practical situation, where the images are comparable, and can be used to evaluate the general performance of diagnostic methods. This data set is referred to as "calibration level 1 fundus images".
The LfED-6D dataset is a collection of 6D grasp annotations acquired through experience (with a robot platform) or by human demonstration. For known objects, the annotated grasps can be directly applied given the pose of the object model is correctly computed. For unknown objects, the grasps can be generalized using methods for shape matching, for example the Dense Geometrical Correspondence Network.
The dataset comprises 16 short sequences showing various objects in challenging backgrounds. The sequences were chosen from a large pool of sequences using a methodology based on clustering visual features of object and background so that those 16 sequences sample evenly well the existing pool. The sequences were annotated by the VOT committee using axis-aligned bounding boxes.
ADHA: “Adverbs Describing Human Actions” is the first benchmark for a new problem — recognizing human action adverbs (HAA). This is the first step for computer vision to change over from pattern recognition to real AI. Some key features of ADHA are: a semantically complete set of adverbs describing human actions, a set of common, describable human actions, and an exhaustive labeling of simultaneously emerging actions in each video.
Affective Text (Test Corpus of SemEval 2007) by Carlo Strapparava & Rada Mihalcea.
A new dataset for sentiment analysis, scraped from Allociné.fr user reviews. It contains 100k positive and 100k negative reviews divided into 3 balanced splits: train (160k reviews), val (20k) and test (20k).
The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under ASEAN IVO as described in this Web page. The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages. ALT now has 13 languages: Bengali, English, Filipino, Hindi, Bahasa Indonesia, Japanese, Khmer, Lao, Malay, Myanmar (Burmese), Thai, Vietnamese, Chinese (Simplified Chinese).
Contain Arabic handwritten digits images (60000 training and 10000 testing images).
It includes 150M (150,211,934) Arabic Web pages.
This is a new image-based handwritten historical digit dataset named ARDIS (Arkiv Digital Sweden). The images in ARDIS dataset are extracted from 15.000 Swedish church records which were written by different priests with various handwriting styles in the nineteenth and twentieth centuries. The constructed dataset consists of three single digit datasets and one digit strings dataset. The digit strings dataset includes 10.000 samples in Red-Green-Blue (RGB) color space, whereas, the other datasets contain 7.600 single digit images in different color spaces.
The first public dataset dedicated for Latin (French) and Arabic Scene Text Detection in Highway panels. It comprises more than 1800 well-annotated images. The dataset was colleted from Moroccan Highway and it has been manually annotated. ASAYAR data can be used to develop and evaluate traffic signs detection and French or Arabic text detection in different languages.
ASSIN (Avaliação de Similaridade Semântica e INferência textual) is a dataset with semantic similarity score and entailment annotations. It was used in a shared task in the PROPOR 2016 conference.
ASSIN2 (Avaliação de Similaridade Semântica e Inferência Textual) is the second edition of a workshop that evaluates Semantic Textual Similarity (STS) and Textual Entailment Recognition (RTE). It was held in conjunction with STIL 2019. Let’s break it down:
Dataset for automatic keyphrase extraction task.
A new large-scale baseball video dataset which is produced semi-automatically by using play-by-play texts available online. The BBDB contains 4200 hours of baseball game videos with 400k temporally annotated activity segments.
This dataset contains 13427 camera images at a resolution of 1280x720 pixels and contains about 24000 annotated traffic lights. The annotations include bounding boxes of traffic lights as well as the current state (active light) of each traffic light. The camera images are provided as raw 12bit HDR images taken with a red-clear-clear-blue filter and as reconstructed 8-bit RGB color images. The RGB images are provided for debugging and can also be used for training. However, the RGB conversion process has some drawbacks. Some of the converted images may contain artifacts and the color distribution may seem unusual.