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, when I try to load the model in lua using
model = loadcaffe.load('deploy.prototxt', 'nin_imagenet.caffemodel', 'ccn2')
I get the following error
Successfully loaded nin_imagenet.caffemodel
MODULE data UNDEFINED
warning: module 'data [type 5]' not found
.../torch/install/share/lua/5.1/ccn2/SpatialConvolution.lua:16: Assertion failed: [math.fmod(nOutputPlane, 16) == 0]. Number of output planes has to be a multiple of 16.
stack traceback:
[C]: in function 'error'
.../torch/install/share/lua/5.1/ccn2/SpatialConvolution.lua:16: in function '__init'
/home/krishnan/torch/install/share/lua/5.1/torch/init.lua:54: in function </home/krishnan/torch/install/share/lua/5.1/torch/init.lua:50>
[C]: in function 'SpatialConvolution'
deploy.prototxt.lua:31: in main chunk
[C]: in function 'dofile'
...hnan/torch/install/share/lua/5.1/loadcaffe/loadcaffe.lua:24: in function 'load'
[string "model = loadcaffe.load('deploy.prototxt', 'ni..."]:1: in main chunk
[C]: at 0x7f13f591ce10
I tried changing the last layer's output to 1024 instead of 1000. Still the deploy.prototxt.lua file generated is the same - it has 1000 and not 1024. I can't quite understand what's happening here. Can anyone please help me?
Thanks