RetinaX dataset is built by selectively combining four publicly available datasets: the STARE dataset, ARIA dataset, RFMiD dataset, and RFMiD 2.0 dataset. It contains a total of 2,514 images and 24 distinct labels, covering nearly all common and rare retinal diseases.
Facilities for the developmentally disabled face the challenge of detecting abnormal behaviors because of limited staff and the difficulty of spotting subtle movements. Traditional methods often struggle to identify these behaviors because abnormal actions are irregular and unpredictable, leading to frequent misses or misclassifications.
The TUROS-TS encompasses 5,357 Google Street View images with 8,775 traffic sign instances covering 9 categories and 28 classes. Three subsets of the dataset were created: test (10%-1050 images 579), validation (20% -1050 images), and training (70% - 3728 images). It is available upon request. If you want to train and test the data set. Please send an email to afef.zwidi@regim.usf.tn
The TUROS-TS encompasses 5,357 Google Street View images with 8,775 traffic sign instances covering 9 categories and 28 classes. Three subsets of the dataset were created: test (10%-1050 images 579), validation (20% -1050 images), and training (70% - 3728 images). It is available upon request. If you want to train and test the data set. Please send an email to afef.zwidi@regim.usf.tn
An automatic waste classification system embedded with higher accuracy and precision of convolution neural network (CNN) model can significantly the reduce manual labor involved in recycling. The ConvNeXt architecture has gained remarkable improvements in image recognition. A larger dataset, called TrashNeXt, comprising 23,625 images across nine categories has been introduced in this study by combining and thoroughly analyzing various pre-existing datasets.
This is a subset of the original GDB-9-Ex_EOM-CCSD dataset at https://doi.org/10.13139/OLCF/2318313. It consists of 100 randomly selected molecules from the original dataset that consists of 80,593 molecules. This dataset contains data-intensive quantum chemical electronic structure calculations for organic molecules of the GDB-9-Ex dataset. Calculations were performed using the Equation of Motion Coupled Cluster (EOM-CCSD) first principles method using the ORCA software. It provides UV-vis spectra calculations of molecules with a high level of accuracy.
This is a subset of the original GDB-9-Ex_TD-DFT-PBE0 dataset at https://doi.org/10.13139/OLCF/2318314. It consists of 100 randomly selected molecules from the original dataset that consists of 96,766 molecules. The dataset contains data-intensive quantum chemical electronic structure calculations for organic molecules of the GDB-9-Ex dataset. Calculations were performed using the Time Dependent Density Functional Theory (TDDFT) first principles method using the ORCA software. It provides UV-vis spectra calculations of molecules with a high level of accuracy.
Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop. To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop. This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.
