This implements training of popular model architectures, such as ResNet, AlexNet, and VGG on the ImageNet dataset.
- Install PyTorch (pytorch.org)
pip install -r requirements.txt- Download the ImageNet dataset and move validation images to labeled subfolders
- To do this, you can use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh
To train a model, run main.py with the desired model architecture and the path to the ImageNet dataset:
python main.py -a resnet18 [imagenet-folder with train and val folders]The default learning rate schedule starts at 0.1 and decays by a factor of 10 every 30 epochs. This is appropriate for ResNet and models with batch normalization, but too high for AlexNet and VGG. Use 0.01 as the initial learning rate for AlexNet or VGG:
python main.py -a alexnet --lr 0.01 [imagenet-folder with train and val folders]usage: main.py [-h] [--arch ARCH] [-j N] [--epochs N] [--start-epoch N] [-b N]
[--lr LR] [--momentum M] [--weight-decay W] [--print-freq N]
[--resume PATH] [-e] [--pretrained]
DIR
PyTorch ImageNet Training
positional arguments:
DIR path to dataset
optional arguments:
-h, --help show this help message and exit
--arch ARCH, -a ARCH model architecture: alexnet | resnet | resnet101 |
resnet152 | resnet18 | resnet34 | resnet50 | vgg |
vgg11 | vgg11_bn | vgg13 | vgg13_bn | vgg16 | vgg16_bn
| vgg19 | vgg19_bn (default: resnet18)
-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)
--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