image-pretraining

Image pre-training

Find the original code at PyTorch ImageNet example.
This adaptation trains the discriminative branch of CortexNet for TempoNet.

Training

To train the discriminative branch of CortexNet, run main.py with the path to an image data set:

python main.py <image data path> | tee train.log

The default learning rate schedule starts at 0.1 and decays by a factor of 10 every 30 epochs.

Usage

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] [--size [S [S ...]]]
               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)
  --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
  --size [S [S ...]]    number and size of hidden layers