We trained a large, deep convolutional neural network to classify the 1.3 million high-
resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes.
On the test data, we achieved top-1 and top-5 error rates of 39.7\% and 18.9\% which is
considerably better than the previous state-of-the-art results. The neural network, which has
60 million parameters and 500,000 neurons, consists of five convolutional layers, some of
which are followed by max-pooling layers, and two globally connected layers with a final …

We trained a large, deep convolutional neural network to classify the 1.2 million high-
resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On
the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively,
which is considerably better than the previous state-of-the-art. The neural network, which
has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some
of which are followed by max-pooling layers, and three fully connected layers with a final …