deep learning - in Torch, why is NIN and VGG19 network 1-top predicting always 670? -


i'm working torch7 on ubuntu, i'm trying apply deep neural network trained on imagenet2012 such nin or vgg19 single image.

this code.

require('image') require('nn') require('pretty-nn')   net = torch.load('nin_test/nin_imagenet_w_model.t7') --net = torch.load('nin_test/vgg_19_w_model.t7') net:evaluate()  images = image.load('nin_test/5ee446f7fdedd01db6c7ea3d43df72b46807911b.thumb.jpeg')   mean= torch.tensor({0.485, 0.456, 0.406}) std =torch.tensor({0.229, 0.224, 0.225})  images3 = image.scale(images, 224, 224,'bilinear')  i=1,3      images3[i] = images3[i]:add(-mean[i]):div(std[i])  end  image.display{image = images3}  output = net:forward(images3):view(-1)  classes indices = torch.sort(output, true) 

when test different images example image of human, cat, dog, bird , etc 1-top predict 670th class or label, means

t7> return  indices[1] 670  

what should do? what's problem?


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