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.
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
- python 3.5
- pytorch
The dataset may be downloaded from here.
Use the following to get help with usage.
python main.py --helpThe results of all the experiments can are compiled in results.pdf. The best model that attained over 76% accuracy on the the test-set can be downloaded from here.