Latest commit 8473762 Sep 25, 2018

README.md

ImageNet training in PyTorch

This implements training of popular model architectures, such as ResNet, AlexNet, and VGG on the ImageNet dataset.

Requirements

Training

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

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