Omkar2118/datasets huggingface dataset:
NOAA/WDS Guyamas Basin
Full name: NOAA/WDS Paleoclimatology - Barron et al. 2004 High Resolution Guaymas Basin Geochemical, Diatom, and Silicoflagellate Data

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoceanography. The data include parameters of paleoceanography with a geographic location of Eastern Pacific Ocean. The time period coverage is from 15190 to 1330 in calendar years before present (BP).
MPM-Verse
Full name: MPMVerse Physics Simulation Dataset

This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, elasticity, jelly, rigid collisions, and melting. Each material is represented as point-clouds that evolve over time. The dataset is designed for learning and predicting MPM-based physical simulations.

Introducing paper: Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs
NACHOS dataset: OCT and Xray

The following datasets: 

Kidney OCT dataset

Three type of tissues were sampled: cortex, medulla, and pelvis. Image size: 185*210 pixels. The OCT dataset comes from 10 porcine kidneys. For each kidney and tissue, there are 30 volumes. Each volume contain 20 images. Total number of images: 10*3*30*20 = 18,000. The same dataset was partitioned in three different levels: image (folder: split_random), volumen (folder: split_volume), and subject (folder: split_subject).

Chest X-ray repository

A chest X-ray repository was built using the ChestX-ray8 dataset, the CheXpert dataset, the MIMIC-CXR dataset, and the PadChest dataset from the TorchXRayVision library. A chest X-ray repository was built using the ChestX-ray8 dataset, the CheXpert dataset, the MIMIC-CXR dataset, and the PadChest dataset from the TorchXRayVision library. The chest X-ray repository was partitioned into four folds using three different partitioning levels. In image-level partitioning(folder: split1_random), images were randomly distributed across four folds. In patient-level partitioning (folder: split2_patient), all images from the same patient were assigned to the same fold. Finally, in dataset-level partitioning (folder: split3_dataset), each dataset was exclusively allocated to a separate fold.

Introducing paper: Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning models
5Char CAPTCHA Dataset

Description:

<a href="https://gts.ai/dataset-download/underpass-image-dataset/" target="_blank">👉 Download the dataset here</a>

The 5Char CAPTCHA Dataset is specifically curated to facilitate the training and testing of machine learning models for CAPTCHA recognition. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) images are a widely used method to prevent bots from interacting with web services by presenting characters or digits that humans can easily recognize but are difficult for automated systems to decode. This dataset includes a collection of CAPTCHA images that consist of five alphanumeric characters, making it ideal for developing models aimed at breaking or solving CAPTCHA challenges.

Dataset Overview:

Total Images: 1000 CAPTCHA images.

Image Format: PNG.

Image Resolution: Each image has a resolution of 180×50 pixels.

Character Count: Every CAPTCHA image contains exactly 5 characters, which could be any combination of uppercase letters (A-Z) and digits (0-9).

Download Dataset

Data Structure:

The dataset is structured into a folder named “captcha_dataset,” which contains 1000 CAPTCHA images. Each image is uniquely named according to the characters it contains. For example, an image with the filename “AB123.png” corresponds to a CAPTCHA where the string “AB123” is displayed. This naming convention makes it easy to extract labels directly from the filenames without needing a separate annotation file.

Potential Applications:

Security Testing: Develop CAPTCHA-solving AI to assess the robustness of CAPTCHA-based security systems.

OCR Enhancement: Use the dataset to improve the accuracy of OCR technologies in recognizing distorted or noisy text in real-world applications.

Web Automation: Implement automated CAPTCHA-solving bots for various web-based tasks like form submissions, web scraping, or accessing restricted content.

CAPTCHA Generation: Train AI to generate new CAPTCHA variations to further enhance security or develop new CAPTCHA systems for websites.

Conclusion:

The 5Char CAPTCHA Dataset is an excellent resource for those working on CAPTCHA recognition, security solutions, and OCR technologies. It presents a range of challenges that can help push the boundaries of existing models, providing an opportunity to improve machine learning techniques in image processing, particularly for security-based applications.

This dataset is sourced from Kaggle.