Load caffemodel and transfer it to chainermodel, then save it & use it to predict label of a image.
If you don't want to compile caffe, there is chainermodel of VGG-16 converted from caffemodel provided here. It's trained on ILSVRC-2014 dataset.
To get it, just run
$ bash download_chainermodel.sh
$ python predict.py
It may produce the below outputs:
['n02124075' 'Egyptian cat'] probability:0.737295150757
['n02123045' 'tabby, tabby cat'] probability:0.17518492043
['n02123159' 'tiger cat'] probability:0.0533684939146
['n02127052' 'lynx, catamount'] probability:0.005824980326
['n04074963' 'remote control, remote'] probability:0.00200812658295
$ bash download_caffemodel.sh
$ python load_model.py