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CNNs achieve 90% accuracy classifying galaxy images

By PulseAugur Editorial · [1 sources] ·

Researchers have evaluated the performance of ResNet101 and InceptionV4 convolutional neural networks for classifying galaxy images. Both models achieved approximately 90% accuracy on the Galaxy10 DECals dataset, demonstrating their suitability for future astronomical surveys. The study found ResNet101 to be slightly superior to InceptionV4 in terms of performance metrics. AI

IMPACT Demonstrates the effectiveness of existing CNN architectures for astronomical image classification, potentially streamlining future research.

RANK_REASON Academic paper detailing model performance on a specific dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CNNs achieve 90% accuracy classifying galaxy images

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Academic paper detailing model performance on a specific dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Lanz Anthonee A. Lagman, Prospero C. Naval Jr, Reinabelle C. Reyes ·

    Classifying galaxies in the Galaxy10 DECals dataset using Inception and Residual CNNs

    arXiv:2606.08826v1 Announce Type: new Abstract: Image data regarding galactic morphology is expected to increase both in quantity and quality for the next foreseeable years; thus it is important to explore which deep learning architectures adapted for image classification tasks a…

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