readme.md

EfficientNet

A PyTorch implementation of EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

[arxiv] [Official TF Repo]

Implement based on Official TF Repo. Only opened EfficientNet is included.
This repo not contains baseline network search(Mnas-Net) and compound coefficient search methods.

This codes are still testing. Some details(HyperParams, transform, EMA ...) are different with Original repo.

How to use:

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

TODO

• Hyper Parameter / Imagenet Transformation Check
• Implementation of Exponetial Moving Average
• Implementation of Learning Rate Step
• Implementation of Resolution Change
• Validation on Imagenet Dataset
• Clean up logging