SpaceNet: A Comprehensive Astronomical Dataset

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SpaceNet: A Comprehensive Astronomical Dataset

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SpaceNet: A Comprehensive Astronomical Dataset

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SpaceNet: A Comprehensive Astronomical Dataset

Description

Explore SpaceNet, a high-quality astronomical image dataset designed for fine-grained and macro classification tasks. With approximately 12,900 samples, advanced augmentation techniques, and synthetic sample generation, SpaceNet supports superior generalization and robust model development for astronomical classification.

Description:

SpaceNet, 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.