Identical & low confidence predictions

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Hi, first of all, huge thanks for creating this repo and training the models!

I’m running into a weird problem, where I can’t seem to run even the most simple example:

from vgg16_places_365 import VGG16_Places365
from keras.preprocessing import image

model = VGG16_Places365(weights='places')

img_path = 'restaurant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

preds = model.predict(x)
print('Predicted:', preds)

The code above is missing the preprocess_input, so I tried with keras, pytorch and my own implementations of it (by guessing what it does). But I keep getting basically the same predictions, doesn’t matter on what image I run them on or what preprocess function I use (all from places365 dataset):

0.023 -> museum/indoor
0.023 -> coffee_shop
0.022 -> art_studio
0.021 -> campus
0.019 -> yard
0.019 -> inn/outdoor
0.016 -> science_museum
0.015 -> motel
0.015 -> building_facade
0.015 -> staircase

Not sure whether that’s a problem with my preprocess_input method or something else, but I’d really appreciate some help!

[atunick]

test model code for VGG16_hybrid_places_1365:
==============================.
import keras
import numpy as np
import os
from VGG16_hybrid_places_1365 import VGG16_Hubrid_1365
from keras.preprocessing import image
from places_utils import preprocess_input
model = VGG16_Hubrid_1365(weights='places', include_top=True)
#model = VGG16_Hubrid_1365(weights='places', include_top=False)
img_path = 'restaurant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
print x.ndim, x.shape
predictions_to_return = 10
preds = model.predict(x)[0]
print 'preds', preds.ndim, preds.shape, np.sort(preds)[::-1][0:10]
top_preds = np.argsort(preds)[::-1][0:10]
print'top_preds', top_preds.ndim, top_preds.shape, top_preds
print '  '
# load the class label
file_name = 'categories_hybrid1365.txt'
if not os.access(file_name, os.W_OK):
    synset_url = 'https://raw.githubusercontent.com/CSAILVision/places365/master/categories_hybrid1365.txt'
    os.system('wget ' + synset_url)
classes = list()
with open(file_name) as class_file:
    for line in class_file:
        classes.append(line.strip().split(' ')[0][3:])
classes = tuple(classes)
print('--SCENE CATEGORIES:')
# output the prediction
for i in range(0, 10):
    print(classes[top_preds[i]])

[GKalliatakis]

This issue is now fixed thanks to the @pavelgonchar and his release with the correct converted weights found in landmark-recognition-challenge.

Also the README file has been updated accordingly.

This issue will now close.