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).
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.
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%) |
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.