jdc08161063/cnn-models

ImageNet pre-trained models with batch normalization

CNN Models by CVGJ

Intro

This repository contains convolutional neural network (CNN) models trained on ImageNet by Marcel Simon at the Computer Vision Group Jena (CVGJ) using the Caffe framework. Each model is in a separate subfolder and contains everything needed to reproduce the results. This repository focuses currently contains the batch-normalization-variants of AlexNet and VGG19 as well as the training code for Residual Networks (Resnet).

How to use

No mean subtraction is required for the pre-trained models! We have a batch-normalization layer which basically does the same.

The pre-trained models can be obtained by the download link written in model_download_link.txt.

If you want to train on your own dataset, simply execute caffe train --solver train.solver --gpu 0 2> train.log to start the training and write the output to the log file train.log.

To evaluate the final model, execute caffe train --solver test.solver --gpu 0 2> test.log.

Accuracy on ImageNet

Single-crop error rates on the validation set of the ILSVRC 2012--16 classification task.

| Model | Top-1 error (vs. original) | Top-5 error (vs. original) | | ------------- |-------------|---------|-------------|---------| | AlexNet_cvgj | 39.9% (vs. 42.6%) | 18.1% (vs. 19.6%) | VGG19_cvgj | 26.9% (vs. 28.7%) | 8.8% (vs. 9.9%) | ResNet10_cvgj | 36.1% | 14.8% | | ResNet50_cvgj | 24.6% (vs. 24.7%) | 7.6% (vs. 7.8%)|

Convergence plots

AlexNet_cvgj

VGG19_cvgj

ResNet10_cvgj

Citation

Please cite the following technical report if our models helped your research:

@article{simon2016cnnmodels,
  Author = {Simon, Marcel and Rodner, Erik and Denzler, Joachim},
  Journal = {arXiv preprint arXiv:1612.01452},
  Title = {ImageNet pre-trained models with batch normalization},
  Year = {2016}
}

The report also contains an overview and analysis of the models shown here.