A comparison of pre-trained CNNs on noisy X-ray fracture detection finds VGG16 far more robust than ResNet50 or EfficientNet, suggesting a complexity-robustness tradeoff.
ImageNet pre-trained models with batch normalization
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abstract
Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches. In this paper, we present a new set of pre-trained models with popular state-of-the-art architectures for the Caffe framework. The first release includes Residual Networks (ResNets) with generation script as well as the batch-normalization-variants of AlexNet and VGG19. All models outperform previous models with the same architecture. The models and training code are available at http://www.inf-cv.uni-jena.de/Research/CNN+Models.html and https://github.com/cvjena/cnn-models
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Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging
A comparison of pre-trained CNNs on noisy X-ray fracture detection finds VGG16 far more robust than ResNet50 or EfficientNet, suggesting a complexity-robustness tradeoff.