基于深度卷积神经网络的AlexNet图片分类

# AlexNet Experiments This repository contains a PyTorch implementation of the AlexNet architeture described in [ImageNet Classification with Deep Convolutional Neural Networks]. The following experiments have been performed on the architecture: - Activation Unit: ReLU vs Tanh - Dropout v/s No Dropout - Overlapping Pooling v/s Non-Overlapping Pooling - Optimization Techniques: SGD v/s SGD with momentum v/s SGD with momentum and weight decay v/s ADAM. The experiments were done on a 35 class subset of the ImageNet Dataset that can be downloaded from [here](https://drive.google.com/file/d/0BzGkOSRHmXLrYjAybXljeDRfak0/view). ## Organisation The code in the repository are organised as follows: - *network.py*: AlexNet arch. implementation - *dataset.py*: custom Pytorch [Dataset] implementation - *train.py*: training/validation/testing code - *main.py*: driver code ## Requirements - python 3.5 - [pytorch] The dataset may be downloaded from [here](https://drive.google.com/file/d/0BzGkOSRHmXLrYjAybXljeDRfak0/view). ## Usage Use the following to get help with usage. ```sh python main.py --help ``` ## Results The results of all the experiments can are compiled in [results.pdf](https://github.com/Shivanshu-Gupta/AlexNet-Experiments/blob/master/Results.pdf). The best model that attained over 76% accuracy on the the test-set can be downloaded from [here](https://drive.google.com/open?id=0By07sE0zY59RRnhVd1VjUURlSWs). [ImageNet Classification with Deep Convolutional Neural Networks]: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf [Dataset]: http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#dataset-class [pytorch]: http://pytorch.org/