Papers › ImageNet Classification with Deep Convolutional Neural Networks
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
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.
Code
23 repositories listed; official and paper-mentioned ones first.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | BP-fMRI-97 | CNN | Accuracy | 54.6% | #5 of 7 | Archive leaderboard | report |
| Graph Classification | BP-fMRI-97 | CNN | F1 | 52.8% | #5 of 7 | Archive leaderboard | report |
| Graph Classification | HIV-DTI-77 | CNN | Accuracy | 54.3% | #5 of 6 | Archive leaderboard | report |
| Graph Classification | HIV-DTI-77 | CNN | F1 | 55.7% | #5 of 6 | Archive leaderboard | report |
| Graph Classification | HIV-fMRI-77 | CNN | Accuracy | 59.3% | #4 of 7 | Archive leaderboard | report |
| Graph Classification | HIV-fMRI-77 | CNN | F1 | 66.3% | #4 of 7 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | DCNN | Percentage correct | 89 | #211 of 265 | Archive leaderboard | report |
| Image Classification | ImageNet ReaL | AlexNet | Accuracy | 62.88% | #53 of 57 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Office-Home | AlexNet [cite:NIPS12CNN] | Accuracy | 54.9 | #18 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Grouped Convolution, Large Kernel Size, Local Response Normalization
1 archive method tag without a method page not shown.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections