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The hybrid CNN vgg16_hybrid_places_1365.py appears to predict only ImageNet classes rather than including neural net predictions for the 365 Places categories.
As an example, with the newly converted training weights provided by Pavel Gonchar, analysis of the image http://places2.csail.mit.edu/imgs/demo/6.jpg results in the following:
--PREDICTED SCENE CATEGORIES:
seashore, coast, seacoast, sea-coast
sandbar, sand bar
swimming trunks, bathing trunks
maillot, tank suit
bikini, two-piece
while using the plain VGG16-places365 model results in the following predictions:
--PREDICTED SCENE CATEGORIES:
desert/sand
desert_road
beach
coast
desert/vegetation
So, clearly both models are working fine. As expected for the same image the hybrid version and the original version will come up with different predictions and clearly the provided image contains not only the beach as a place but a person wearing a swimming trunk etc.
Your problem for getting different results might lie in the fact that your
cache_subdir='models'
might contain the weights from the previous version. So try deleting all previous weight files and running the scripts again before reporting your new results.
Clearly these are not the Places classes suggested by G Kalliatakis in his code vgg16_hybrid_places_1365.py, i.e,,
As another example, the vgg16_hybrid_places_1365 CNN results for the attached "beach" image are:
Again, where are the Places class label predictions?
PLEASE ADVISE.
THANK YOU.
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