deeplabv3语义分割迁移代码

## Transfer Learning for Semantic Segmentation using PyTorch DeepLab v3 This repository contains code for Fine Tuning [DeepLabV3 ResNet101](https://arxiv.org/abs/1706.05587) in PyTorch. The model is from the [torchvision module](https://pytorch.org/docs/stable/torchvision/models.html#semantic-segmentation). The tutorial can be found here: [https://towardsdatascience.com/transfer-learning-for-segmentation-using-deeplabv3-in-pytorch-f770863d6a42?sk=b403331a7b30c02bff165a93823a5524](https://towardsdatascience.com/transfer-learning-for-segmentation-using-deeplabv3-in-pytorch-f770863d6a42?sk=b403331a7b30c02bff165a93823a5524) I've fine tuned the model for the [CrackForest](https://github.com/cuilimeng/CrackForest-dataset) data-set. The model was fine tuned for 25 epochs and achieves an testing AUROC value of 0.842. The segmentation output of the model on a sample image are shown below.  ### Installing dependencies #### Using pip ``` pip install -r requirements.txt ``` #### Using conda ``` conda env create -f environment.yml ``` ### Usage of the module ``` Usage: main.py [OPTIONS] Options: --data-directory TEXT Specify the data directory. [required] --exp_directory TEXT Specify the experiment directory. [required] --epochs INTEGER Specify the number of epochs you want to run the experiment for. Default is 25. --batch-size INTEGER Specify the batch size for the dataloader. Default is 4. --help Show this message and exit. ``` To run the code with the CrackForest dataset and store the results in folder called CFExp use the following command. ``` python main.py --data-directory CrackForest --exp_directory CFExp ``` The datahandler module has two functions for creating datasets fron single and different folders. 1. ``` def get_dataloader_sep_folder(data_dir: str, image_folder: str = 'Image', mask_folder: str = 'Mask', batch_size: int = 4) ``` Create Train and Test dataloaders from two separate Train and Test folders. The directory structure should be as follows. ``` data_dir --Train ------Image ---------Image1 ---------ImageN ------Mask ---------Mask1 ---------MaskN --Test ------Image ---------Image1 ---------ImageM ------Mask ---------Mask1 ---------MaskM ``` 2. ``` def get_dataloader_single_folder(data_dir: str, image_folder: str = 'Images', mask_folder: str = 'Masks', fraction: float = 0.2, batch_size: int = 4) ``` Create from a single folder. The structure should be as follows. ``` --data_dir ------Image ---------Image1 ---------ImageN ------Mask ---------Mask1 ---------MaskN ``` The repository also contains a JupyterLab file with the loss and metric plots as well as the sample prediction code. ## Citation If you found this repository to be useful and use it in your work, please consider citing it: Bibtex Entry: @misc{minhas_2019, title={Transfer Learning for Semantic Segmentation using PyTorch DeepLab v3}, url={https://github.com/msminhas93/DeepLabv3FineTuning}, journal={GitHub.com/msminhas93}, author={Minhas, Manpreet Singh}, year={2019}, month={Sep}} IEEE Format Citation: M. S. Minhas, “Transfer Learning for Semantic Segmentation using PyTorch DeepLab v3,” GitHub.com/msminhas93, 12-Sep-2019. [Online]. Available: https://github.com/msminhas93/DeepLabv3FineTuning.