Deep Learning Methods for Semantic Segmentation

The main aim of the study is to develop an algorithm based Deep Learning methods for Semantic Segmentation of aerial images (or road scenarios), which allows determine objects on images with acceptable accuracy and inference speed close to real-time conditions.

PyTorch-ENet

PyTorch (v1.0.0) implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, ported from the lua-torch implementation ENet-training created by the authors. The actual source code of PyTorch-ENet is here.

Installation

1. Python 3 and pip.
2. Set up a virtual environment (optional, but recommended).
3. Install dependencies using pip: pip install -r requirements.txt.

Usage

Run main.py, the main script file used for training and/or testing the model. The following options are supported:

python main.py [-h] [--mode {train,test,full}] [--resume]
               [--batch-size BATCH_SIZE] [--epochs EPOCHS]
               [--learning-rate LEARNING_RATE] [--lr-decay LR_DECAY]
               [--lr-decay-epochs LR_DECAY_EPOCHS]
               [--weight-decay WEIGHT_DECAY] [--dataset {camvid,cityscapes}]
               [--dataset-dir DATASET_DIR] [--height HEIGHT] [--width WIDTH]
               [--weighing {enet,mfb,none}] [--with-unlabeled]
               [--workers WORKERS] [--print-step] [--imshow-batch]
               [--device DEVICE] [--name NAME] [--save-dir SAVE_DIR]

For help on the optional arguments run: python main.py -h

Examples: Training

python main.py -m train --save-dir save/folder/ --name model_name --dataset name --dataset-dir path/root_directory/

Examples: Resuming training

python main.py -m train --resume True --save-dir save/folder/ --name model_name --dataset name --dataset-dir path/root_directory/

Examples: Testing

python main.py -m test --save-dir save/folder/ --name model_name --dataset name --dataset-dir path/root_directory/

Project structure

Folders

• data: Contains instructions on how to download the datasets and the code that handles data loading.
• metric: Evaluation-related metrics.
• models: ENet model definition.
• save: By default, main.py will save models in this folder. The pre-trained models can also be found here.

Files

• args.py: Contains all command-line options.
• main.py: Main script file used for training and/or testing the model.
• test.py: Defines the Test class which is responsible for testing the model.
• train.py: Defines the Train class which is responsible for training the model.
• transforms.py: Defines image transformations to convert an RGB image encoding classes to a torch.LongTensor and vice versa.