Satellite Road Segmentation

This repo contains a PyTorch an implementation of semantic segmentation models for Massuchusetts Roads Dataset

Installation

pip install -r requirements.txt

Training

To train a model, set the corresponding configuration file, then simply run:

python train.py --config config.json

The log files will be saved in saved\runs and the .pth chekpoints in saved\

tensorboard --logdir saved

Code structure

main-repository/
│
├── train.py - main script to start training
├── trainer.py - the main trained
├── config.json - holds configuration for training
│
├── base/ - abstract base classes
│   ├── base_data_loader.py
│   ├── base_model.py
│   ├── base_dataset.py - All the data augmentations are implemented here
│   └── base_trainer.py
│
├── dataloader/ - loading the data for different segmentation datasets
│
├── models/ - contains semantic segmentation models
│
├── saved/
│   ├── runs/ - trained models are saved here
│   └── log/ - default logdir for tensorboard and logging output
│  
└── utils/ - small utility functions
    ├── losses.py - losses used in training the model
    ├── metrics.py - evaluation metrics used
    └── lr_scheduler - learning rate schedulers 

Acknowledgement

The repository is a derivative of pytorch-segmentation and has been used for experimentation of Satellite Image Segmentation on Massuchussets Datset.