A multi-stage data augment approach based on transfer learning algorithm.
The code is tested with Python 3.6, CUDA 10.1, Pytorch 1.5 on win10.
To train the model from scratch, use the following code:
Part1 添加数据1.ipynb.py # Alpha blending and Gaussian Fusion are carried out
Part1 增加数据2.ipynb # Add data to one folder
To train the model with transfer learning, use the following code:
python 训练网络.py # training the domain source
Part2 使用ImageNet的预处理 对F4K 随机图片预测.ipynb # The code for the random selected training
Part2 使用mobileNetV2 训练F4K 挑选数据.ipynb # The code for the data picked and image augmentation training
Training at source domain(/source domain performance.jpg) Training at target domain(/target domain performance.jpg)