Artificial Intelligence
The dataset includes five subclasses of white blood cells: eosinophils, basophils, neutrophils, monocytes, and lymphocytes. The types and positions of white blood cells in the images have been manually and accurately annotated using an annotation tool, and the annotation information files are stored in .txt, .xml, and .csv formats to meet the format requirements of different network model training for annotation files.
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The dataset provides a collection of image-based classification and regression problems for artificial intelligence, under extreme visual attention constraints. Spatial shapes with simple geometries and called geometrons are embedded in an intense visual texture stream, with the aim of investigating the limits of artificial visual attention to capture known or unknown, but ghost or buried shapes.
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The Universal Networking Language (UNL) is a pioneering framework designed to facilitate seamless communication and knowledge sharing across different languages and cultures. This UNL French Dictionary focuses specifically on the rich and diverse world of French cuisine, offering a structured representation of culinary terms, ingredients, cooking techniques, and descriptors in French alongside their universal equivalents.
Purpose and Importance:
The UNL French Dictionary serves several key purposes:
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The Universal Networking Language (UNL) serves as a conceptual framework aimed at facilitating communication across different languages and cultures. In the context of culinary arts, the UNL dictionary provides a structured approach to represent Indian culinary terms, ingredients, cooking methods, and descriptors in a universally understandable manner.
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The dataset has undergone format conversion based on URPC2021_Sonar_images_data, enabling it to be trained by YOLO and RT-DETR models.
The folder 'images' contains image files
The folder 'labels' contains TXT format annotation files.
The annotation file in the folder annotations is in XML format
Data.yaml is the configuration file for YOLO training
Data_deTR is the configuration file for RT-DETR and US-DETR training
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The SINEW (Sensors in Home for Elderly Wellbeing) dataset consists of 15 high-level biomarker features, derived from raw sensor readings collected by in-home sensors used for predictive modeling research: SINEW Weekly Biomarker.
This dataset was collected for a study focused on the early detection of mild cognitive impairment, providing an opportunity for timely intervention before it progresses to Alzheimer's disease.
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The SINEW (Sensors in Home for Elderly Wellbeing) dataset consists of 15 high-level biomarker features, derived from raw sensor readings collected by in-home sensors used for predictive modeling research: SINEW 15 - Monthly Biomarker.
This dataset was collected for a study focused on the early detection of mild cognitive impairment, providing an opportunity for timely intervention before it progresses to Alzheimer's disease.
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# Top 100 YouTube Channels Dataset
## Overview
This dataset provides comprehensive information about the top 100 YouTube channels based on subscriber count. It offers valuable insights into the most popular content creators on the platform, their performance metrics, and channel details.
## Dataset Contents
The dataset includes the following information for each channel:
- Channel ID
- Title
- Custom URL
- Subscriber Count
- Video Count
- View Count
- Category
- Country
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This study presents a English-Luganda parallel corpus comprising over 2,000 sentence pairs, focused on financial decision-making and products. The dataset draws from diverse sources, including social media platforms (TikTok comments and Twitter posts from authoritative accounts like Bank of Uganda and Capital Markets Uganda), as well as fintech blogs (Chipper Cash and Xeno). The corpus covers a range of financial topics, including bonds, loans, and unit trust funds, providing a comprehensive resource for financial language processing in both English and Luganda.
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This dataset, mentioned in paper "MS2A: Memory Storage-to-Adaptation for Cross-domain Few-annotation Object Detection" and prepared for Cross-domain Few-annotation Object Detection task, consists of two cross-domain scenarios: Indus-S to Indus-T1 and Indus-S to Indus-T2. In detail, Indus-S consists of 4614 images for training and 1153 images for validation; Indus-T1 and Indus-T2 have 269 and 432 images for validation respectively. For the training data of Indus-T1 and Indus-T2, we introduce three different settings: 10-anno, 30-anno and 50-anno.
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