README.md

EfficientNet-Pytorch

A demo for train your own dataset on EfficientNet Thanks for the >A PyTorch implementation of EfficientNet, I just simply demonstrate how to train your own dataset based on the EfficientNet-Pytorch.

Step 1：Prepare your own classification dataset

---

Then the data directory should looks like:

-dataset\
    -model\
    -train\
        -1\
        -2\
        ...
    -test\
        -1\
        -2\
        ...

Step 2: train and test

python efficientnet_sample.py

--data-dir : (str) Path of /dataset folder. Default: None

--num-epochs : (int) Number of epochs for training. Default: 40

--batch-size : (int) Batch size. Default: 4

--img-size : (int) Selected size for image to be resized. Default: [1024,1024]

--class-num : (int) Number of classes in dataset. Default: 3

--weights-loc : (str) Path of weights to be loaded. If None, pretrained weights will automatically be downloaded & loaded. Default: None Example: "...//weights.pth//"

--lr : (float) Learning rate. Default: 0.01

--net-name : (str) States which efficientnet model will be used. Used for downloading pretrained weights as well.

--resume-epoch : (int) Defines starting epoch. Default: 0

--momentum : (float) Sets momentum. Default: 0.9

Example usage: python ".\efficientnet_sample.py" --data-dir "D:\\ml_data\\dataset" --num-epochs 80 --batch-size 4 --img-size 896 --class-num 3 --weights-loc "D:\\ML\\efficientnet-b3-birads.pth" --lr 0.01 --net-name "efficientnet-b3" --resume-epoch 40

The pre-trained model is available on >release. You can download them under the folder eff_weights.

(3)You can get the final results and the best model on dataset/model/.