CNNs achieve 90% accuracy classifying galaxy images

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 →

• Galaxy10 DECals dataset
• InceptionV4
• Lanz Anthonee Lagman
• ResNet101

• paper
• other

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