In the name of God
This repository contains implementation of some deep neural networks by Keras.
LeNet-5 was one of the earliest convolutional neural networks and promoted the development of deep learning. Since 1988, after years of research and many successful iterations, the pioneering work has been named LeNet-5.
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AlexNet competed in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up. The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training.
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VGG is a classical convolutional neural network architecture. It was based on an analysis of how to increase the depth of such networks. The network utilises small 3 x 3 filters. Otherwise, the network is characterized by its simplicity: the only other components being pooling layers and a fully connected layer.
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The MNIST database of handwritten digits, has a training set of 60000 examples, and a test set of 10000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
You can download pre-trained models from this page:
- Download LeNet-5 Pre-trained Model
- Download AlexNet Pre-trained Model
- Download VGG Pre-trained Model
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- LeNet-5: http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
- AlexNet: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
- VGG: https://arxiv.org/abs/1409.1556v6
- MNIST Dataset: http://yann.lecun.com/exdb/mnist/
- CIFAR-10 Dataset: https://www.cs.toronto.edu/~kriz/cifar.html