REVIEW 2 cited by
ImageNet pre-trained models with batch normalization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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
Forward citations
Cited by 2 Pith papers
-
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.
-
Self-supervised Data Bootstrapping for Deep Optical Character Recognition of Identity Documents
An iterative bootstrapping method that mixes synthetic and model-mined real character images raises identity-document OCR accuracy from 58.1% to 99.4% on 74 classes.
Discussion (0). Continue with ORCID to comment.