Advertising on the Telegram channel «Datasets | Machine Learning | Projects»

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

❗️ JAY HELPS EVERYONE EARN MONEY!$29,000 HE'S GIVING AWAY TODAY! Everyone can join his channel and make money! He gives away from $200 to $5.000 every day in his channel https://t.me/+LgzKy2hA4eY0YWNl ⚡️FREE ONLY FOR THE FIRST 500 SUBSCRIBERS! FURTHER ENTRY IS PAID! 👆👇 https://t.me/+LgzKy2hA4eY0YWNl

🟢 Name Of Dataset: MIT-BIH Arrhythmia Database 🟢 Description Of Dataset: The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings were chosen at random from a set of 4000 24-hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were selected from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample.The recordings were digitized at 360 samples per second per channel with 11-bit resolution over a 10 mV range. Two or more cardiologists independently annotated each record; disagreements were resolved to obtain the computer-readable reference annotations for each beat (approximately 110,000 annotations in all) included with the database.This directory contains the entire MIT-BIH Arrhythmia Database. About half (25 of 48 complete records, and reference annotation files for all 48 records) of this database has been freely available here since PhysioNet's inception in September 1999. The 23 remaining signal files, which had been available only on the MIT-BIH Arrhythmia Database CD-ROM, were posted here in February 2005.Much more information about this database may be found in theMIT-BIH Arrhythmia Database Directory. 🟢 Official Homepage: https://physionet.org/content/mitdb/1.0.0/ 🟢 Number of articles that used this dataset: 31 🟢 Dataset Loaders: Not found 🟢 Articles related to the dataset: 📝 Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach 📝 ECG Heartbeat Classification: A Deep Transferable Representation 📝 Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification 📝 Subject-Aware Contrastive Learning for Biosignals 📝 Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation 📝 A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance 📝 AQuA: A Benchmarking Tool for Label Quality Assessment 📝 Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN 📝 Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization 📝 MedFuncta: Modality-Agnostic Representations Based on Efficient Neural Fields ================================== 🔴 For more datasets resources: ✓ https://t.me/Datasets1

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

🟢 Name Of Dataset: RVL-CDIP 🟢 Description Of Dataset: TheRVL-CDIPdataset consists of scanned document images belonging to 16 classes such as letter, form, email, resume, memo, etc. The dataset has 320,000 training, 40,000 validation and 40,000 test images. The images are characterized by low quality, noise, and low resolution, typically 100 dpi.Source:Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning 🟢 Official Homepage: https://www.cs.cmu.edu/~aharley/rvl-cdip/ 🟢 Number of articles that used this dataset: Unknown 🟢 Dataset Loaders: huggingface/datasets (rvl_cdip): https://huggingface.co/datasets/rvl_cdip huggingface/datasets (rvl-cdip_easyOCR): https://huggingface.co/datasets/jordyvl/rvl-cdip_easyOCR huggingface/datasets (rvl_cdip): https://huggingface.co/datasets/aharley/rvl_cdip huggingface/datasets (rvl_cdip_easyocr): https://huggingface.co/datasets/jordyvl/rvl_cdip_easyocr huggingface/datasets (rvl_cdip_mini): https://huggingface.co/datasets/dvgodoy/rvl_cdip_mini ================================== 🔴 For more datasets resources: ✓ https://t.me/Datasets1

🟢 Name Of Dataset: FUNSD (Form Understanding in Noisy Scanned Documents) 🟢 Description Of Dataset: Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking.Source:FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents 🟢 Official Homepage: https://guillaumejaume.github.io/FUNSD/ 🟢 Number of articles that used this dataset: Unknown 🟢 Dataset Loaders: huggingface/datasets: https://huggingface.co/datasets/nielsr/FUNSD_layoutlmv2 mindee/doctr: https://mindee.github.io/doctr/latest/datasets.html#doctr.datasets.FUNSD ================================== 🔴 For more datasets resources: ✓ https://t.me/Datasets1

🟢 Name Of Dataset: IIIT-AR-13K 🟢 Description Of Dataset: IIIT-AR-13K is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories - table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection.Source:IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents 🟢 Official Homepage: http://cvit.iiit.ac.in/usodi/iiitar13k.php 🟢 Number of articles that used this dataset: 6 🟢 Dataset Loaders: Not found 🟢 Articles related to the dataset: 📝 Deep learning for table detection and structure recognition: A survey 📝 RanLayNet: A Dataset for Document Layout Detection used for Domain Adaptation and Generalization 📝 The YOLO model that still excels in document layout analysis 📝 IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents 📝 Document AI: Benchmarks, Models and Applications 📝 Robust Table Detection and Structure Recognition from Heterogeneous Document Images ================================== 🔴 For more datasets resources: ✓ https://t.me/Datasets1

Tired of endless job boards and low offers? Unlock access to exclusive remote jobs from top startups—some with salaries $100k+ and early-bird roles at $50/h and above. New high-paying openings posted daily—tech, marketing, design, and more. Ready to upgrade your career from anywhere? Check today’s top jobs now before they’re gone! #إعلان InsideAds

Looking for a $10k–$15k/month remote job? Top international startups post new offers DAILY. Land high-paying roles in tech, marketing, design & more — most never seen elsewhere. Want early access before everyone else? Get today’s exclusive jobs list — new positions every morning! Don’t miss your next career breakthrough. Join now! #إعلان InsideAds

🚀 THE 7-DAY PROFIT CHALLENGE! 🚀 Can you turn $100 into $5,000 in just 7 days? Jay can. And she’s challenging YOU to do the same. 👇 https://t.me/+QOcycXvRiYs4YTk1 https://t.me/+QOcycXvRiYs4YTk1 https://t.me/+QOcycXvRiYs4YTk1