caffe.Classifier() and caffe.Net() have different class predictions for same image?

[NightFury13]

Is there a difference in the way the predictions work in the case of caffe.Classifier() and caffe.Net()? I have an image (linked below) that when run in the caffe-example (classification.ipynb) gives a prediction of class 287 (net.predict([img]).argmax()). However, if I use this image in the another example (filter_visualization.ipynb) the class probabilites (net.blobs['prob'].argmax()) comes out to be 2! Is there something trivial that I am missing or this really shouldn't be happening? Which of these is the correct classification if at all one is correct (I have tweaked the cat image a little bit to see what are the effects on classification)?

I am using the bvlc_reference_caffenet.caffemodel and the corresponding deploy.prototxt with the imagenet mean image provided in caffe as default (ilsvrc12).

Sample cat image :

[seanbell]

Yes, the two are very different, and you should expect different answers. Look at the code -- Classifier.predict does oversampling and calls Net.forward_all: https://github.com/BVLC/caffe/blob/master/python/caffe/classifier.py#L47

Also, in the future please ask for usage help on the mailing list. From https://github.com/BVLC/caffe/blob/master/CONTRIBUTING.md:

Please do not post usage, installation, or modeling questions, or other requests for help to Issues.
Use the caffe-users list instead. This helps developers maintain a clear, uncluttered, and efficient view of the state of Caffe.