Summary of Which Backbone to Use: a Resource-efficient Domain Specific Comparison For Computer Vision, by Pranav Jeevan and Amit Sethi
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision
by Pranav Jeevan, Amit Sethi
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive evaluation of various lightweight, pre-trained convolutional neural network (CNN) backbones across different datasets and domains. The study assesses the performance of these backbones under consistent training settings on a range of datasets, including natural images, medical images, galaxy images, and remote sensing images. The authors aim to provide insights into the performance trade-offs and effectiveness of different backbones, enabling machine learning practitioners to make informed decisions about model selection for computer vision applications. The findings suggest that some CNN architectures, such as ConvNeXt, RegNet, and EfficientNet, consistently perform well across diverse domains, while attention-based architectures tend to struggle under low-data fine-tuning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers tested many different types of pre-trained neural networks (called “backbones”) to see how they work on various images. They wanted to find the best backbone for a specific task, like recognizing galaxies or medical images. The results show that some backbones are better than others at doing this job. For example, ConvNeXt and EfficientNet do well across many types of images. But attention-based networks don’t do as well when they have limited training data. This study helps people choose the right backbone for their specific task. |
Keywords
» Artificial intelligence » Attention » Cnn » Fine tuning » Machine learning » Neural network
