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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worksheets/0xfafccca5
Colab
Datasets
CIFAR-10
ImageNet
Office-Home
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Graph Classification | BP-fMRI-97 | CNN | Accuracy | 54.6% | # 5 | |
| F1 | 52.8% | # 6 | ||||
| Image Classification | CIFAR-10 | DCNN | Percentage correct | 89 | # 195 | |
| Graph Classification | HIV-DTI-77 | CNN | Accuracy | 54.3% | # 5 | |
| F1 | 55.7% | # 4 | ||||
| Graph Classification | HIV-fMRI-77 | CNN | Accuracy | 59.3% | # 4 | |
| F1 | 66.3% | # 4 | ||||
| Image Classification | ImageNet ReaL | AlexNet | Accuracy | 62.88% | # 53 |
