efficientnets_pytorch

Code rewriting TF Implementation of EfficientNet to PyTorch

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

[arxiv] [Official Github Repo]

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HOW TO:

python3 main.py -h
usage: main.py [-h] --save_dir SAVE_DIR [--root ROOT] [--gpus GPUS]
               [--num_workers NUM_WORKERS] [--model {b0}] [--epoch EPOCH]
               [--batch_size BATCH_SIZE] [--test]
               [--dropout_rate DROPOUT_RATE]
               [--dropconnect_rate DROPCONNECT_RATE] [--optim {adam,rmsprop}]
               [--lr LR] [--beta [BETA [BETA ...]]] [--momentum MOMENTUM]
               [--eps EPS] [--decay DECAY]

Pytorch EfficientNet

optional arguments:
  -h, --help            show this help message and exit
  --save_dir SAVE_DIR   Directory name to save the model
  --root ROOT           The Directory of data path.
  --gpus GPUS           Select GPU Numbers | 0,1,2,3 |
  --num_workers NUM_WORKERS
                        Select CPU Number workers
  --model {b0}          The type of Efficient net.
  --epoch EPOCH         The number of epochs
  --batch_size BATCH_SIZE
                        The size of batch
  --test                Only Test
  --dropout_rate DROPOUT_RATE
  --dropconnect_rate DROPCONNECT_RATE
  --optim {adam,rmsprop}
  --lr LR               Base learning rate when train batch size is 256.
  --beta [BETA [BETA ...]]
  --momentum MOMENTUM
  --eps EPS
  --decay DECAY

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