Confused with output shape and prediction in multi class classification

I have a model that trained on to predict one of the 14 class,

But when I go for prediction for a single image,

imageData ='RGB')
imageData = self.transformSequence(imageData)
imageData = imageData.unsqueeze_(0)
input = torch.autograd.Variable(imageData)
output = self.model(input.cuda())

The output shape is [1,1024,7,7].

Very very weird than Keras predict. how can I know the output label???

the output isn’t a prediction, it’s just a feature map of size NCHW, you need to add MaxPooling/Linear layers to do classification.

After forward passing it should be outputting the class prediction, as it contains,

       features = self.features(x)
       out = F.relu(features, inplace=True)
       out = F.adaptive_avg_pool2d(out, (1, 1)).view(features.size(0), -1)
       out = self.classifier(out)
       return out

But it returning the same shape.