Omkar2118/datasets huggingface dataset:
JNU Bearing Dataset

The JNU Bearing Dataset, developed by Jiangnan University in China, is widely used in the field of fault diagnosis for rotating machinery. It contains high-resolution vibration signals collected from single-row spherical roller bearings, specifically types N205 and NU205. These signals were recorded under three different rotational speeds: 600, 800, and 1000 revolutions per minute (rpm), which allows the dataset to be used in scenarios involving varying operating conditions.

The data were sampled at a high frequency of 50 kHz, and each signal segment spans 20 seconds, offering sufficient temporal resolution to capture subtle fault characteristics. The dataset includes four primary health conditions: healthy bearings, inner-ring faults, outer-ring faults, and ball (rolling-element) faults. Each of these fault types is represented at each of the three operating speeds, resulting in a total of 12 distinct class combinations.

To prepare the data for training machine learning models, the continuous vibration signals are often segmented into smaller overlapping samples, typically containing 1,024 or 2,048 data points. Depending on the study, the number of samples per class may vary, but a common configuration involves approximately 600 to 976 samples per class, divided into training, validation, and test sets, often using a 60/20/20 split.

Despite the relatively small size of the dataset compared to larger industrial benchmarks, its high quality and structured fault conditions make it a valuable resource for evaluating the performance of deep learning models, such as convolutional neural networks, transformers, and domain adaptation methods. Researchers often augment the data through overlapping windowing or advanced generative models to enhance the robustness of their fault classification models. The variation in rotational speeds also makes the dataset well-suited for tasks involving domain generalization and transfer learning.
Asanyukta Kathak Mudra

Description:

<a href="https://gts.ai/dataset-download/asanyukta-kathak-mudra/" target="_blank">👉 Download the dataset here</a>

The Asanyukta Kathak Mudra Gestures Dataset was meticulously collected to train a model on recognizing Kathak Mudras, aiding my little sister in perfecting her dance form by identifying errors and other challenges. This dataset includes images of various Asamyukta Hasta Mudras from Kathak, a classical Indian dance form. It is designed to support the development of AI models that can recognize and analyze these intricate gestures, serving as a valuable resource for both cultural preservation and technological advancement.

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Applications and Use Cases

Gesture Recognition: Train machine learning models to accurately recognize and classify different Kathak mudras.

Cultural Preservation: Utilize AI technology to preserve and document traditional dance forms, ensuring they remain accessible for future generations.

Educational Tools: Develop innovative tools to assist Kathak practitioners in learning, practicing, and perfecting their gestures with real-time feedback.

Art and Technology Integration: Explore and enhance the intersection of classical arts and modern technology, fostering new forms of artistic expression.

How to Use the Dataset

Download the Dataset: Access and download the dataset from Kaggle or the provided source.

Set Up Your Environment: Ensure all necessary dependencies and software are installed in your development environment.

Train Your Model: Utilize the training images from the dataset to train your machine learning model. Use validation images to fine-tune hyperparameters and prevent overfitting.

Test Your Model: Evaluate your model’s performance using the test images to ensure accuracy and reliability in recognizing Kathak mudras.

This dataset is sourced from Kaggle.
Car Camera Photos

Description:

<a href="https://gts.ai/dataset-download/car-camera-photos/" target="_blank">👉 Download the dataset here</a>

This dataset comprises output photos captured by Car Camera Photos installed in various positions on a car, offering a comprehensive collection of visual data essential for advancing 
self-driving car technologies. Each camera provides unique perspectives, capturing frames from videos that reflect different angles and positions around the vehicle. This multi-angle approach ensures a rich and varied dataset, covering diverse scenarios encountered on the road. The frames are meticulously annotated to identify and label various objects such as vehicles, pedestrians, traffic signs, lane markings, and other critical elements necessary for the development of accurate and reliable object detection models.

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Designed specifically for object detection tasks, this dataset plays a pivotal role in training and validating the algorithms used in autonomous driving systems. The diversity of the captured scenes includes various weather conditions, times of day, and complex traffic situations, making the dataset robust and versatile. Researchers and developers can leverage this dataset to enhance the perception capabilities of self-driving cars, improving their ability to interpret and navigate real-world environments. By providing detailed annotations and high-quality visual data, this dataset is an invaluable resource for pushing the boundaries of autonomous vehicle technology and ensuring safer, more efficient self-driving solutions.

This dataset is sourced from Kaggle.
DataSeeds.AI-Sample-Dataset-DSD

Dataset Summary
The DataSeeds.AI Sample Dataset (DSD) is a high-fidelity, human-curated computer vision-ready dataset comprised of 7,772 peer-ranked, fully annotated photographic images, 350,000+ words of descriptive text, and comprehensive metadata. While the DSD is being released under an open source license, a sister dataset of over 10,000 fully annotated and segmented images is available for immediate commercial licensing, and the broader GuruShots ecosystem contains over 100 million images in its catalog.

Each image includes multi-tier human annotations and semantic segmentation masks. Generously contributed to the community by the GuruShots photography platform, where users engage in themed competitions, the DSD uniquely captures aesthetic preference signals and high-quality technical metadata (EXIF) across an expansive diversity of photographic styles, camera types, and subject matter. The dataset is optimized for fine-tuning and evaluating multimodal vision-language models, especially in scene description and stylistic comprehension tasks.

Technical Report - Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery
Github Repo - Access the complete weights and code which were used to evaluate the DSD -- https://github.com/DataSeeds-ai/DSD-finetune-blip-llava
This dataset is ready for commercial/non-commercial use.
Dataset Structure
Size: 7,772 images (7,010 train, 762 validation)
Format: Apache Parquet files for metadata, with images in JPG format
Total Size: ~4.1GB
Languages: English (annotations)
Annotation Quality: All annotations were verified through a multi-tier human-in-the-loop process
anno-lexical

https://huggingface.co/datasets/mediabiasgroup/anno-lexical

Introducing paper: The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection