ImageNet Classification with Deep Convolutional Neural Networks

Alex KrizhevskyIlya SutskeverGeoffrey E. Hinton

   Papers with code   Abstract  PDF

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... (read more)

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