DIRS24.v1 presents a dataset captured in campus environment. These images are curated suitably for the utilization in developing perception modules. These modules can be very well employed in Advanced Driver Assistance Systems (ADAS). The images of dataset are annotated in diversified formats such as COCO-MMDetection, Pascal-VOC, TensorFlow, YOLOv7-PyTorch, YOLOv8-Oriented Bounding Box, and YOLOv9.



the first digitalized mammogram dataset for breast cancer in Saudi Arabia, depend on the BI-RADS categories, to solve the availability problem of local public datasets by collecting, categorizing, and annotating mammogram images, supporting the medical field by providing physicians with different diagnosed cases especially in Saudi Arabia


For the semantic segmentation to be effectively done, a labelled flood scene image dataset was created. This initiative was undertaken with official permission obtained from the BBC News Website and YouTube channel, providing a valuable dataset for our research. We were granted permission to use flood-related videos for research purposes, ensuring ethical and legal considerations. Specifically, videos were sourced from the BBC News YouTube channel. The obtained videos were then processed to extract image frames, resulting in a dataset comprising 10,854 images.


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This dataset is derived from Sentinel-2 satellite imagery.
The main goal is to employ this dataset to train and classify images into two classes: with trees, and without trees.
The structure of the dataset is 2 folders named: "tree" (images containing trees) and "no-trees" (images without presence of trees).
Each folder contains 5200 images of this type.


This work presents a large-scale three-fold annotated, low-cost microscopy image dataset of potato tubers for plant cell analysis in deep learning (DL) framework which has huge potential in the advancement of plant cell biology research. Indeed, low-cost microscopes coupled with new-generation smartphones could open new aspects in DL-based microscopy image analysis, which offers several benefits including portability, ease of use, and maintenance.


This dataset consists of 3500 images of beach litter and 3500 corresponding pixel-wise labelled images. Although performing such pixel-by-pixel semantic masking is expensive, it allows us to build machine-learning models that can perform more sophisticated automated visual processing. We believe this dataset may be of significance to the scientific communities concerned with marine pollution and computer vision, as this dataset can be used for benchmarking in the tasks involving the evaluation of marine pollution with various machine learning models.



Videos contain a high volume of texts and are broadcasted via different sources, such as television, the internet, etc. Since optical character recognition (OCR) engines are script-dependent, script identification is the precursor for them. Depending on the video sources, identification of video scripts is not trivial since we have difficult issues, such as low resolution, complex background, noise, blur effects, etc. In this work, a deep learning-based system named as LWSINet: LightWeight Script Identification Network (6-layered CNN) is proposed to identify the video scripts.


Radio frequency interference (RFI) is a problem in microwave remote sensing even for sensors operating in the protected band at 1.4 GHz (L-band).  Unfortunately, little is known about the sources of the interference, which complicates the design of systems to deal with it.  The SMAP radiometer is unique in that it comprises an array of tools to detect RFI, including spectral information and kurtosis in addition to the more conventional time-domain thresholding.  This data set contains examples used in an investigation to determine if the kurtosis (K) can be combined with the information abo


This dataset consists of both non-retinal detachment and rhegmatogenous retinal detachment fundus images.  The fundus images were collected from the four eye hospital in the country (namely India) such as Silchar medical college and hospital (Assam), Aravind eye hospital (Tamil Nadu), LV prasad eye hospital (Hyderabad), and Medanta- The medicity (Gurugram).  A total of 1693 images have been collected from these hospitals of which 1017 fundus images belonged to retinal detachments and the rest 676 were non-retinal detachments.