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| id | 6c7d087b-ad67-4e36-8210-28b445d4d11b |
| application_area | ImageNet |
| task | Classification |
| task_extended | ImageNet classification |
| data_type | Image/Photo |
| data_source | http://www.image-net.org/challenges/LSVRC/2012/ |
| title | ImageNet Classification with Deep Convolutional Neural Networks |
| source | Advances in Neural Information Processing Systems |
| url | http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf |
| year | 2012 |
| authors | Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton |
| abstract | 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% 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 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently developed regularization method called 'dropout' that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. |
| google_scholar | https://scholar.google.com/scholar?cites=2071317309766942398&as_sdt=40000005&sciodt=0,22&hl=en |
| bibtex | @incollection{NIPS2012_4824, title = {ImageNet Classification with Deep Convolutional Neural Networks}, author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E}, booktitle = {Advances in Neural Information Processing Systems 25}, editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger}, pages = {1097--1105}, year = {2012}, publisher = {Curran Associates, Inc.}, url = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf} } |
| description | AlexNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012. |
| provenance | https://github.com/onnx/models/tree/master/bvlc_alexnet |
| architecture | Convolutional Neural Network (CNN) |
| learning_type | Supervised learning |
| format | .onnx |
| I/O | model I/O can be viewed here |
| license | model license can be viewed here |
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