name: Network in Network Imagenet Model
caffemodel: nin_imagenet.caffemodel
caffemodel_url: https://www.dropbox.com/s/cphemjekve3d80n/nin_imagenet.caffemodel?dl=1 license: BSD
caffe_commit: pull request yet to be merged
gist_id: d802a5849de39225bcc6
This model is a 4 layer Network in Network model trained on imagenet dataset.
Thanks to the replacement of fully connected layer with a global average pooling layer, this model has greatly reduced parameters, which results in a snapshot of size 29MB, compared to AlexNet which is about 230MB, it is one eighth the size.
The top 1 performance of this model on validation set is 59.36%, which is slightly better than AlexNet. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)
The training time of the model is also greatly reduced compared to AlexNet because of the faster convergence. It takes 4-5 days to train on a GTX Titan.
BSD
Hi @mavenlin. I am using the NiN architecture to train ImageNet 2012. After about 50k iterations, the validation accuracy is around 0.1% which corresponds to random chance. I am using the same structure and initialization as you have. Can you please let me know when (iteration number) the validation accuracy starts to increase? This will help me decide if the network is learning anything useful and if I should restart with different hyperparameters.
Thanks.