ImageNet Classification with Deep Convolutional Neural Networks

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 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of convolutional nets. To reduce overfitting in the globally connected layers we employed a new regularization method that proved to be very effective.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Benchmark
Graph Classification BP-fMRI-97 CNN Accuracy 54.6% # 6
F1 52.8% # 7
Image Classification CIFAR-10 DCNN Percentage correct 89 # 177
Graph Classification HIV-DTI-77 CNN Accuracy 54.3% # 6
F1 55.7% # 4
Graph Classification HIV-fMRI-77 CNN Accuracy 59.3% # 4
F1 66.3% # 4
Image Classification ImageNet AlexNet Top 1 Accuracy 63.3% # 718
Top 5 Accuracy 84.6% # 259
Number of params 60M # 551
Hardware Burden 2G # 1
Operations per network pass 0.07G # 1
Image Classification ImageNet ReaL AlexNet Accuracy 62.88% # 51

Results from Other Papers


Task Dataset Model Metric Name Metric Value Rank Uses Extra
Training Data
Source Paper Compare
Unsupervised Domain Adaptation Office-Home AlexNet [cite:NIPS12CNN] Accuracy 54.9 # 6

Methods