Omkar2118/datasets huggingface dataset:
ECG-Image-Database
Full name: Digitization and Classification of ECG Images: The George B. Moody PhysioNet Challenge 2024

The George B. Moody PhysioNet Challenges are annual competitions that invite participants to develop automated approaches for addressing important physiological and clinical problems. The 2024 Challenge invites teams to develop algorithms for digitizing and classifying electrocardiograms (ECGs) captured from images or paper printouts. Despite the recent advances in digital ECG devices, physical or paper ECGs remain common, especially in the Global South. These physical ECGs document the history and diversity of cardiovascular diseases (CVDs), and algorithms that can digitize and classify these images have the potential to improve our understanding and treatment of CVDs, especially for underrepresented and underserved populations.
Kitchen Items in AI2-THOR

This dataset comprises over 9,000 images captured in the AI2-THOR simulation environment, featuring 69 distinct object classes. It includes variations of certain objects, such as raw bread and cooked bread, to enhance diversity and realism. The images were collected using AI2-THOR's built-in tools and subsequently preprocessed and formatted for compatibility with YOLOv5, making it suitable for object detection tasks in simulated environments.

Introducing paper: AI-Driven Relocation Tracking in Dynamic Kitchen Environments
simco-comco

These datasets, ComCo and SimCo, designed for evaluating multi-object representation in Vision-Language Models (VLMs). These datasets provide controlled environments for analyzing model biases, object recognition, and compositionality in multi-object scenarios.

ComCo: Composed of real-world objects derived from the COCO dataset.
SimCo: Contains simple geometric shapes in structured multi-object settings.

Introducing paper: CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation
robot swarms solving a coverage task

Simulations and hardware experiments of different coverage tasks saved as robot swarm objects. The coverage runs can be loaded and visualized. Additionally, the anomaly detection method can be applied to each run, as shown in example.ipynb.

Introducing paper: Discovering Antagonists in Networks of Systems: Robot Deployment
LegalCore

Recognizing events and their coreferential men- tions in a document is essential for understand- ing semantic meanings of text. The existing re- search on event coreference resolution is mostly limited to news articles. In this paper, we present the first dataset for the legal domain, LegalCore, which has been annotated with comprehensive event and event coreference in- formation. The legal contract documents we an- notated in this dataset are several times longer than news articles, with an average length of around 25k tokens per document. The anno- tations show that legal documents have dense event mentions and feature both short-distance and super long-distance coreference links be- tween event mentions. We further benchmark mainstream Large Language Models (LLMs) on this dataset for both event identification and event coreference resolution tasks, and find that this dataset poses significant challenges for both open-source and proprietary LLMs, which all perform significantly worse than a su- pervised baseline.

Introducing paper: LegalCore: A Dataset for Legal Documents Event Coreference Resolution
MCiteBench

MCiteBench is a benchmark to evaluate multimodal citation text generation in Multimodal Large Language Models (MLLMs).

Introducing paper: MCiteBench: A Multimodal Benchmark for Generating Text with Citations
Unaligned-VL-CMU-CD (neighbor distance 2)

Street-View images captured at different timestamps often undergo geometric transformations. To make the VL-CMU-CD dataset more challenging and closer to real-world applications, we generate Unaligned VL-CMU-CD by  pairing images with their adjacent neighbors within the same sequence. In this dataset, the adjacent neighbor distance is set to 2 to set to 2 to ensure a distinct difference from the original VL-CMU-CD.

Introducing paper: Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms
Vulnerable Captchas

Description:

<a href="https://gts.ai/dataset-download/vulnerable-captchas/" target="_blank">👉 Download the dataset here</a>

This dataset focuses on an interesting example of weak CAPTCHA implementations, highlighting potential security vulnerabilities in systems that rely on simple alphanumeric captchas. CAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are widely used to protect websites from bots and automated scripts. However, not all CAPTCHA implementations are equally secure, and some are prone to exploitation through automated processes.

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Context

The inspiration for this dataset came from a personal experience while accessing a website I frequently use, which I will refer to as “System” for privacy reasons. I wanted to automate a repetitive task on the site using a Python script, but I was initially blocked by a CAPTCHA that was required to complete the login process. CAPTCHAs are generally effective in stopping bots, especially those like Google’s reCAPTCHA, which are difficult to bypass with machine learning models due to their sophisticated design.

However, in this case, the CAPTCHA images were simple enough for human eyes to decipher, consisting only of clearly readable alphanumeric characters. The challenge intrigued me, and as I was simultaneously reading “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron, I decided to use this scenario as an opportunity to apply my newly acquired knowledge in machine learning.

Problem and Approach

The dataset captures images of these vulnerable CAPTCHA challenges and provides annotations for each. During the process of automating the CAPTCHA resolution, I learned that the system did not just rely on the image itself. Upon inspection of the HTML, I found that the CAPTCHA content was hashed and stored inside a hidden form field. Which could easily be manipulate to bypass the verification entirely.

Key Learnings

CAPTCHA Design Matters: Not all CAPTCHAs are created equal. Simpler alphanumeric CAPTCHAs can often be defeated by image recognition models or form manipulation.

Image Classification: This dataset offers a collection of label CAPTCHA images.  That could be use to train image classification models aimed at recognizing and solving CAPTCHAs automatically.

Security Implications: The project sheds light on the importance of implementing proper security mechanisms beyond just CAPTCHA images.  Such as encryption, hashing, and verification strategies that prevent easy manipulation.

Practical Approach: Sometimes, simpler solutions such as analyzing the webpage structure and finding security loopholes can be more efficient than complex machine learning models.

This dataset is sourced from Kaggle.
Khmer Word Detection Dataset

Description:

<a href="https://gts.ai/dataset-download/khmer-word-detection-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset was designed to train machine learning models capable of performing word detection within Khmer language documents. It aims to support the development of advanced techniques for identifying and retrieving specific words in large-scale textual collections, especially those written in Khmer script.

Context

Word detection, often referred to as keyword spotting, is a fundamental task in the field of document analysis and recognition. It involves the automatic identification of specific words or phrases within a document, enabling fast information retrieval from extensive text corpora. Although word detection has been widely researched in many languages, there has been a significant rise in interest toward applying these techniques to Khmer, the official language of Cambodia.

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Content

The dataset consists of various types of Khmer text data, providing examples that highlight the intricacies of the language’s structure. The goal is to improve the machine learning model’s ability to accurately detect and isolate individual words within these documents, regardless of formatting variations.

This dataset is crucial for several practical applications. It enables users to search vast digital collections or archives, allowing quick access to specific information in Khmer texts. This capability can be particularly useful for libraries, educational institutions, and research organizations where time-efficient document retrieval is essential. Moreover, keyword spotting can assist in extracting data from historical documents, contributing to the preservation of Cambodia’s cultural heritage. Automatic word detection technology could help digitize and analyze ancient Khmer manuscripts, ensuring their longevity and accessibility to future generations.

Potential Uses

Digital Archiving: Automating the process of searching and retrieving information from large collections of Khmer-language books, newspapers, or other printed materials.

Cultural Preservation: Assisting in the digitization of ancient Khmer manuscripts, enabling their preservation, analysis, and wider dissemination.

Research: Enhancing the efficiency of Khmer document retrieval in academic and historical research.

Commercial Applications: Streamlining document management in sectors like government, healthcare, and legal services that rely heavily on Khmer-language documents.

Conclusion

The development of effective word detection for Khmer-language documents has the potential to revolutionize the way we manage, search, and analyze textual data in Khmer. By overcoming the challenges posed by its complex script, this dataset offers a stepping stone toward more sophisticated and efficient language processing tools for the Khmer language.

This dataset is sourced from Kaggle.