name: Network in Network Imagenet Model
caffe_commit: pull request yet to be merged
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.