This implements training of popular model AlexNet on the ImageNet dataset.
- Install PyTorch (pytorch.org)
- Download the ImageNet dataset from http://www.image-net.org/
- Then, and move validation images to labeled subfolders, using the following shell script
To train a alexnet model, run train.py with the desired model architecture and the path to the ImageNet dataset:
python3 train.py -p 10 -b 256 --epochs 10 /scratch/sudheer.achary/Imagenet-orig/ [imagenet-folder with train and val folders]usage: main.py [-h] [-j N] [--epochs N] [--start-epoch N] [-b N] [--lr LR] [--momentum M] [--weight-decay W] [--print-freq N] [--resume PATH] [-e] [--pretrained][--seed SEED] DIR PyTorch ImageNet Training positional arguments: DIR path to dataset optional arguments: -h, --help show this help message and exit -j N, --workers N number of data loading workers (default: 4) --epochs N number of total epochs to run --start-epoch N manual epoch number (useful on restarts) -b N, --batch-size N mini-batch size (default: 256), this is the total batch size of all GPUs on the current node when using Data Parallel or Distributed Data Parallel --lr LR, --learning-rate LR initial learning rate --momentum M momentum --weight-decay W, --wd W weight decay (default: 1e-4) --print-freq N, -p N print frequency (default: 10) --resume PATH path to latest checkpoint (default: none) -e, --evaluate evaluate model on validation set --pretrained use pre-trained model --seed SEED seed for initializing training. 
