Omkar2118/datasets huggingface dataset:
CADEdgeTune

CAD-EdgeTune dataset is acquired using a Husarion ROSbot 2.0 and ROSbot 2.0 Pro with the collection speed set to 5 frames per second from a suburban university environment. We may split the information into subgroups for noon, dusk, and dawn in order to depict our surroundings under various lighting situations. We have assembled 17 sequences totaling 8080 frames, of which 1619 have been manually analyzed using an open-source pixel annotation program. Since nearby photographs are highly similar to one another, we decide to annotate every five images. Since the annotation procedure may be highly time-consuming, we employ soft-labeling while annotating CAD-EdgeTune dataset, which enables us to proceed through the frames more quickly. The annotation method we employ enables us to create minute annotations inside an image's objects, and the categorization would encompass related pixels. This approach may result in less-than-perfect annotations and some performance accuracy loss, but the loss would not be great enough to justify producing pixel-perfect annotations.
3 Sources
Full name: 3 Sources Dataset

We provide here a new multi-view text dataset, collected from three well-known online news sources: BBC, Reuters, and The Guardian. This dataset exhibits a number of common aspects of multi-view problems highlighted previously -- notably that certain stories will not be reported by all three sources (i.e incomplete views), and the related issue that sources vary in their coverage of certain topics (i.e. partially missing patterns).
SpaceNet: A Comprehensive Astronomical Dataset

Description:

<a href="https://gts.ai/dataset-download/spacenet-a-comprehensive-astronomical-dataset/" target="_blank">👉 Download the dataset here</a>

SpaceNet: A Comprehensive Astronomical Dataset, obtained via a novel double-stage augmentation framework called FLARE is a hierarchically structured and high-quality astronomical image dataset. It is meticulously designed for both fine-grained and macro classification tasks. Comprising approximately 12,900 samples, SpaceNet incorporates lower (LR) to higher resolution (HR) conversion with standard augmentations and a diffusion approach for synthetic sample generation. This comprehensive dataset enables superior generalization on various recognition tasks, including classification.

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Key Features

High-Resolution Images: The dataset includes high-quality images that facilitate accurate analysis and classification.

Hierarchical Structure: The dataset is hierarchically organized to support both macro and fine-grained classification tasks.

Advanced Augmentation Techniques: Utilizes FLARE framework for double-stage augmentation, enhancing the dataset’s diversity and robustness.

Synthetic Sample Generation: Employs a diffusion approach to create synthetic samples, boosting the dataset’s size and variability.

Usage

SpaceNet is ideal for:

Training and Evaluation: Developing and testing machine learning models for fine-grained and macro astronomical classification tasks.

Research: Exploring hierarchical classification approaches within the astronomy domain.

Model Development: Creating robust models capable of generalizing across both in-domain and out-of-domain datasets.

Educational Purposes: Providing a rich dataset for educational projects in astronomy and machine learning.

This dataset is sourced from Kaggle.