19,997 machine learning datasets
19,997 dataset results
potential use
The dataset has railway track images of two types: normal and defective
for greenhouse mapping
hair styles in sketches
郭俊杰
This COVID-19 dataset consists of Non-COVID and COVID cases of both X-ray and CT images. The associated dataset is augmented with different augmentation techniques to generate about 17099 X-ray and CT images. The dataset contains two main folders, one for the X-ray images, which includes two separate sub-folders of 5500 Non-COVID images and 4044 COVID images. The other folder contains the CT images. It includes two separate sub-folders of 2628 Non-COVID images and 5427 COVID images.
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Sakha-TB is a de-identified image dataset of frontal chest X-rays (CXR), collected through collaboration with several medical institutions in the Republic of Sakha (Yakutia, Russia). The set contains 400 normal X-rays and 400 X-rays with manifestations of pulmonary tuberculosis, balanced to some extent by age and gender, in 16-bit and 8-bit lossless PNG format, converted directly from DICOM files without any changes.
Dataset contains annotated photographs of pear orchard for object detection tasks using YOLO architecture. It contains only one class "Pear fruits". This dataset is distributed under license (CC BY 4.0).
Dataset contains annotated photographs of pear fruitlets for object detection tasks using YOLO architecture. It has only one class "Pear fruitlet". The digital images of pear fruitlets were collected in the experimental site of the Institute of Horticulture (LatHort) with cultivars ‘Suvenirs’, ‘Vasarine Sviestine’ and ‘Mramornaya’ on seedling rootstocks ‘Kazraushu’ with planting distances 4×5 m (500 trees per 1 ha). (Krimūnu parish, Dobeles district, Latvia: 56.610169, 23.305956). The collection of fruitlet images of ‘Suvenirs’, ‘Vasarine Sviestine’ and ‘Mramornaya’ was done at the beginning of August (79 days after full bloom). The collection of digital images was carried out using a photo camera of mobile device Huawei P 40: 50 MP Ultra Vision Camera (Wide Angle, f/1.9 aperture) + 16 MP Ultra-Wide Angle Camera (f/2.2 aperture) + 8 MP Telephoto Camera (f/2.4 aperture, OIS), the image size: 3000×4000 px; 5.0 MP. The collection of images was carried out in field conditions in 2022, in t
The photo fixation of cherry fruitlets was done in the LatHort orchard in Dobele, at the development of fruit (BBCH stage 72). BBCH-scale describes the phenological development of grapes: 7 - development of fruit; 72 - fruit size up to 20 mm. Two photo images were taken for each tree – perpendicularly, in a tree-facing view and in an oblique view. The images were annotated using the tool makesense.ai. Then the annotated images 3008x2000 were automatically cropped out on 640x640 images with overlap 30% and validated manually. The images were saved in YOLO format.
The photo fixation of cherry fruits was done in the LatHort orchard in Dobele, at the beginning of fruit coloration (BBCH stage 81). BBCH-scale for grapes describes the phenological development of grapes: 8 - ripening of berries; 81 - beginning of ripening: berries begin to develop variety-specific colour. Two photo images were taken for each tree – perpendicularly, in a tree-facing view and in an oblique view. The images were annotated using the tool makesense.ai. Then the annotated images 6016x4000 were automatically cropped out on 640x640 images with overlap 30% and validated manually. The images were saved in YOLO format.
The photo fixation of apple fruitlets was done in the LatHort orchard in Dobele, at the development of fruit (BBCH stage 76-78). BBCH-scale describes the phenological development of grapes: 7 - development of fruit; 76 - fruit about 60% final size; 78 - fruit about 80% final size. Two photo images were taken for each tree – perpendicularly, in a tree-facing view and in an oblique view. The images were annotated using the tool makesense.ai. Then the annotated images 3008x2000 were automatically cropped out on 640x640 images with overlap 30% and validated manually. The images were saved in YOLO format.
The photo fixation of apple fruits was done in the LatHort orchard in Dobele, at the maturity of fruit and seed (BBCH stage 81-85). BBCH-scale describes the phenological development of grapes: 8 - maturity of fruit and seed; 81 - beginning of ripening; 85 - advanced ripening. Two photo images were taken for each tree – perpendicularly, in a tree-facing view and in an oblique view. The images were annotated using the tool makesense.ai. Then the annotated images 3008x2000 were automatically cropped out on 640x640 images with overlap 30% and validated manually. The images were saved in YOLO format.
test
This dataset contains 10089 Bengali comments and its tag( Nostalgic and Non-nostalgic)
Consists of 36,785 images belonging to a diverse 92 classes. This class count is significantly higher than publicly available datasets. Maintains a low-class imbalance and a highly comprehensive data distribution for robust model training. It also provides the remote sensing community with an extra platform to validate the performance on multiple benchmarks.
This dataset contains a collection of texts from publications from a broad range of social science domains (e.g., economics, politics, psychology, etc.). The texts are annotated with labels for Survey Item Linking (SIL), an Entity Linking (EL) task. SIL is divided into two sub-tasks: Mention Detection (MD), a binary text classification task, and Entity Disambiguation (ED), a sentence similarity task. Sentences that mention survey items are labeled with the IDs of entities from a knowledge base (GSIM). SILD contains 20,454 sentences in English and German from 100 publications.
The Tagalog Universal Dependencies NewsCrawl dataset consists of annotated text extracted from the Leipzig Tagalog Corpus. Data included in the Leipzig Tagalog Corpus were crawled from Tagalog-language online news sites by the Leipzig University Institute for Computer Science.
Towards automated analysis of large environments, hyperspectral sensors must be adapted into a format where they can be operated from mobile robots. In this dataset, we highlight hyperspectral datacubes collected from the Hyper-Drive imaging system. Our system collects and registers datacubes spanning the visible to shortwave infrared (660-1700 nm) in 33 wavelength channels. The system also simultaneously captures the ambient solar spectrum reflected off a white reference tile. The dataset consists of 500 labeled datacubes from on-road and off-road terrain compliant with the ATLAS.