Omkar2118/datasets huggingface dataset:
PMC-Patients

Retrieval-based Clinical Decision Support (ReCDS) can aid clinical workflow by providing relevant literature and similar patients for a given patient. However, the development of ReCDS systems has been severely obstructed by the lack of diverse patient collections and publicly available large-scale patient-level annotation datasets. In this paper, we collect a novel dataset of patient summaries and relations called PMC-Patients to benchmark two ReCDS tasks: Patient-to-Article Retrieval (ReCDS-PAR) and Patient-to-Patient Retrieval (ReCDS-PPR). Specifically, we extract patient summaries from PubMed Central articles using simple heuristics and utilize the PubMed citation graph to define patient-article relevance and patient-patient similarity. PMC-Patients contains 167k patient summaries with 3.1 M patient-article relevance annotations and 293k patient-patient similarity annotations, which is the largest-scale resource for ReCDS and also one of the largest patient collections. Human evaluation and analysis show that PMC-Patients is a diverse dataset with high-quality annotations. We also implement and evaluate several ReCDS systems on the PMC-Patients benchmarks to show its challenges and conduct several case studies to show the clinical utility of PMC-Patients.
RLM25
Full name: Research-Level Mathematics 2025

RLM25 is an evaluation benchmark containing 619 paired examples of research-level natural language mathematical statements and their corresponding Lean formalizations. The examples are drawn from six real-world formalization projects, and each entry includes essential context along with timestamp information to ensure freshness and avoid data contamination. Its primary purpose is to serve as a challenging testbed for assessing autoformalization systems, helping researchers measure improvements in translating complex, research-level mathematics into formal code.

Introducing paper: Improving Autoformalization using Type Checking
ProofNet#

ProofNet# is an evaluation benchmark derived from the original ProofNet, which contains 371 paired examples of informal undergraduate mathematical statements and their corresponding formalizations. Updated for Lean 4, ProofNet# corrects formalization errors and retains the original structure and content.

Introducing paper: Improving Autoformalization using Type Checking
ProofNetVerif

ProofNetVerif is an evaluation benchmark comprising 3,752 entries, each including an informal mathematical statement, its reference formalization, a predicted formalization, and a binary label indicating semantic equivalence. It is designed to assess autoformalization metrics by providing a challenging testbed for both reference-based and reference-free evaluation approaches.

Introducing paper: Improving Autoformalization using Type Checking
Traffic Sign Recognition YOLOv8

Description:
The Traffic Sign Recognition Dataset is designed to support the development of deep learning models, particularly for object detection and classification. The dataset includes images of various traffic signs, each annotated with bounding boxes and corresponding class labels. These images have been preprocessed for uniform size and pixel normalization, ensuring optimal training conditions for models like YOLOv8. The dataset captures a wide variety of traffic signs, making it ideal for tasks related to traffic safety and autonomous vehicle systems.

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Applications
The dataset is well-suited for training models in autonomous driving, transportation safety, and smart traffic management systems. It can be used in various environments to improve the recognition of traffic signs in different weather conditions, lighting, and angles. This diversity enhances the dataset’s robustness, making it useful for real-time object detection tasks in smart city infrastructure and road safety systems.

Dataset Structure

Image Annotations: Each traffic sign in the dataset is mark with bounding boxes, allowing precise localization during model training.
Class Labels: The dataset includes class labels for different types of traffic signs, such as stop signs, speed limits, and warnings.
Preprocessing: The images have been resize and normalize to maintain consistent pixel values, which aids in model training.
Use Cases
This dataset can be extend beyond traffic sign recognition. It can be used in conjunction with other datasets for multi-class object detection in road environments, facilitating the development of comprehensive autonomous navigation systems. Additionally, the dataset supports research into improving model accuracy in challenging conditions such as low-light, occluded signs, or varying weather.

Why Use YOLOv8?

YOLOv8 is a highly efficient model for object detection, offering speed and accuracy improvements over its predecessors. The architecture of YOLOv8 enables quick real-time detection of traffic signs with high precision, making this dataset particularly suitable for applications where rapid recognition is essential.

Future Improvements
Further development of this dataset could involve incorporating more challenging real-world conditions, such as damage or obscured signs, or training models to handle multiple sign types in a single image. The dataset can also be expand to cover more countries, including region-specific signs and regulations.

Visit Here: https://gts.ai/dataset-download/traffic-sign-recognition-yolov8/

This dataset is sourced from Kaggle.
XMIDI

XMIDI is a comprehensive, large-scale symbolic music dataset that includes accurate emotion and genre labels, consisting of 108,023 MIDI files. The average duration of the music pieces is approximately 176 seconds, yielding a total dataset length of around 5,278 hours.

Introducing paper: XMusic: Towards a Generalized and Controllable Symbolic Music Generation Framework
NL2SH-ALFA

https://arxiv.org/abs/2502.06858

Introducing paper: LLM-Supported Natural Language to Bash Translation
CytoImage Net Dataset

Description:
CytoImageNet is an extensive collection of microscopy images, carefully curated to aid in the development of fast and automated methods for analyzing biological data. With over 890,000 grayscale images spanning 894 diverse classes, it addresses the increasing demand for high-throughput image-based biological assays.

Visit Here: https://gts.ai/dataset-download/cytoimage-net-dataset/
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Motivation:
As advancements in microscopy imaging fuel new discoveries, the challenge of processing large volumes of image data has grown significantly. CytoImageNet draws inspiration from ImageNet’s success in computer vision, offering a large-scale resource specifically for biological imaging. Pretraining deep learning models on CytoImageNet has demonstrated competitive performance, producing features optimized for microscopy classification tasks. The combination of CytoImageNet with ImageNet-based features now sets the benchmark for bioimage transfer learning.

Dataset Composition:
CytoImageNet comprises 890,737 grayscale microscopy images, divided across 894 classes, with approximately 1,000 images per class. These images span a broad range of biological contexts, including cell morphology, tissue structures, and organoid assays, sourced from major biological image repositories. Each image is weakly-labeled, ensuring scalability for various tasks while still maintaining biological relevance.

Why CytoImageNet Matters:
Pretraining on biological image data accelerates the development of models tailored for specific microscopy tasks, improving classification accuracy and interpretability in bioimage analysis. CytoImageNet not only enhances the capacity of models to extract meaningful biological information but also fosters innovation in areas like drug discovery, disease diagnosis, and biomedical research.

Key Features:
890,737 images, all grayscale and microscopy-focused.
894 distinct classes, approximately 1,000 images per class.
Curated from 40 open-access datasets, ensuring diverse biological representations.
Designed for bioimage pretraining, providing competitive features for transfer learning.
Incorporates both general and highly specific biological contexts, making it a versatile tool for various research applications.
CytoImageNet represents a new frontier in microscopy image analysis, empowering researchers to unlock insights from vast biological datasets with precision and scalability. Pretraining models on CytoImageNet enhances performance in downstream tasks, setting a new standard for bioimage feature extraction.

This dataset is sourced from Kaggle.
Anime Style Transfer Dataset

Description:
The Anime Style Dataset is ideal for training AI models to perform style transformation between real human faces and anime-style illustrations. It contains two main folders:

<a href="https://gts.ai/dataset-download/anime-style-transfer-dataset/" target="_blank">👉 Download the dataset here</a>
Training Set: 820 pairs of real faces and their anime-style representations.
Test Set: 93 similar pairs for validation.
Applications: This dataset supports various AI research areas, including style transfer, GANs, and creative projects in animation and gaming industries.

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Enhanced Features:
Advanced Augmentation: To diversify the dataset, techniques such as flipping, rotation, or color changes can be applied.
Metadata Enrichment: Including annotations for facial features, pose, and lighting conditions can help develop more nuanced models capable of capturing specific anime styles.
Use Cases:
Digital Content Creation: Automating the conversion of real-life photos into anime avatars for media or gaming.
Artistic Tools: Enabling new tools for anime artists by simplifying style transition between real faces and animated ones.
AI-driven Filters and Augmented Reality: Expanding its use in creative social media filters and personalized avatars for various platforms.
This dataset is sourced from
Horse Racing Photo Dataset

Description:
<a href="https://gts.ai/dataset-download/horse-racing-photo-dataset/" target="_blank">👉 Download the dataset here</a>
This dataset provides a detailed collection of horse race photo finishes from PMU events. It is ideal for machine learning and computer vision research, particularly in image recognition and sports analytics.

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Image Resolutions:
Each image is captured and made available in three sizes:

Small (200×96 pixels): Optimized for quick, low-resolution analysis.

Medium (450×217 pixels): Balances detail and file size for moderate analysis.

Large (478×230 pixels): High-resolution images for precise, in-depth research.

CSV Metadata:

Accompanying the images, a CSV file contains critical metadata about each race:

Walk Type: Differentiates between Trot and Gallop, fundamental gait types in horse racing, essential for analyzing movement patterns.

Speciality & Discipline: These sub-categories give further details on the race type, providing researchers with more context for analysis.

Rope Direction: The position of the track’s barrier is noted as either on the left or right side of the horses, influencing lane dynamics and photo finish placement.

Weather Conditions: Detailed weather codes allow insights into the environmental conditions affecting race visibility and performance:

P1 – P17: Codes range from sunny and cloudy to adverse weather like thunderstorms and snow, offering a broad spectrum for training models on different environmental factors.

Additional Environmental Factors:
Luminosity: Whether the race occurred during day or night is an important factor, providing training data for models that operate under various lighting conditions, enhancing the dataset’s versatility.
Potential Applications:

This dataset is suitable for numerous machine learning applications:

Photo Finish Analysis: Machine learning models can be trained to detect race winners based on these images.

Environmental Impact Studies: The weather data enables research into how different weather conditions affect race outcomes.

Gait Classification: Using the trot and gallop metadata, researchers can develop algorithms that classify different horse movements automatically.

Why This Dataset Stands Out:

Versatility: With varied image sizes, weather conditions, and gait types, this dataset supports a broad range of research.

Rich Metadata: The dataset provides thorough race information that offers a deeper context for understanding the nuances of horse racing.

This dataset provides an excellent resource for building models related to sports analytics, environmental conditions, and gait classification. The variety in race conditions and photo finish details ensures its suitability for complex machine learning projects.

This dataset is sourced from Kaggle.
Portuguese Meals Dataset

Description:

<a href="https://gts.ai/dataset-download/portuguese-meals-dataset/" target="_blank">👉 Download the dataset here</a>

The Portuguese Meals Dataset is a carefully curated collection of 6,771 images across 23 food categories, designed to assist machine learning systems in recognizing Portuguese meals and performing nutritional calculations. This dataset is particularly valuable for supporting dietary needs, such as guiding diabetic patients toward healthier food choices, while also enriching AI food recognition technologies.

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Dataset Structure

The dataset contains a mix of traditional Portuguese dishes and popular international food items. Dishes like Bacalhau à Brás, Cozido à Portuguesa, and Francesinha represent Portugal’s diverse culinary heritage, while international favorites such as pizza, hamburgers, and donuts offer a broader perspective.

Traditional Portuguese Dishes:

Aletria (Sweet Vermicelli Dessert)

Bacalhau à Brás (Salted Cod with Potatoes)

Caldo Verde (Portuguese Kale Soup)

Feijoada à Transmontana (Bean Stew)

Francesinha (Portuguese Sandwich)

Jardineira de Carne (Meat Stew)

International Dishes:

Pizza

Hamburger

Ice Cream

Hotdog

Waffles

Data Collection Sources


The images in this dataset were collected from a variety of sources:

Food-101 Dataset: Contributed categories like fries, chocolate cake, hotdogs, and ice cream.

Google Images: Used for capturing authentic images of traditional Portuguese meals.

Restaurant Apps: Apps such as Zomato, TripAdvisor, and The Fork helped enrich the dataset with images sourced from local restaurants, ensuring high quality and relevance.

Application

This dataset is primarily designed for AI and machine learning projects focused on food recognition, nutritional analysis, and health monitoring. It is particularly suited for applications that cater to individuals with specific dietary needs, such as diabetes management. Additionally, the dataset is an excellent resource for cultural studies or AI applications aiming to promote Portuguese gastronomy.


Enhancements and Future Prospects

Further enhancements to this dataset could include:

Adding nutritional data for each dish, allowing for a more comprehensive analysis.

Expanding the dataset to cover more local dishes from underrepresented regions in Portugal.

Incorporating real-time meal recognition technology through video data capture for more dynamic AI applications.

Conclusion

The Portuguese Meals Dataset is a versatile and culturally rich resource that bridges the gap between local cuisine and cutting-edge AI technology. By focusing on both traditional and popular dishes, it enables accurate and context-aware food recognition systems, aiding not only in healthcare but also in the promotion of Portuguese culinary heritage.

This dataset is sourced from Kaggle.