ml-project-2-libtrip's Introduction

Machine Learning: Project 2 - Road Segmentation

The goal of this project is to segment satellite images by detecting roads. Our classifier consists of a convolutional neural network called UNet.

Team members

Ahmad Bilal KAKAR
Imane Zaaraoui
Lina Bousbina

Installation

To run the code of this project, you need to install the libraries listed in the requirements.txt file. You can perform the installation using this command:

pip install -r requirements.txt

Dependencies:

matplotlib
numpy
pillow
scikit-image
torch
torchvision
tqdm

Predictions for AIcrowd

To reproduce our submission on AIcrowd, run:

python run.py

This command will create the predicted mask for each test image in the predictions directory. It will also produce the submission.csv file for submission.

Structure

This is the structure of the repository:

data: contains the datasets
models: contains the trained models
normal_training.ipynb: if you want to train the model on the original training set, run this notebook until the cell where we save the model. It also performs predictions, saves predicted masks, creates submission files, and applies post-processing
augmented_data_training: similar to normal_training.ipynb, but with data augmentation during training.
cross_validation.ipynb: performs cross-validation to determine the optimal split ratio for training and validation.
run.py: script for making predictions for AIcrowd
helper.py: contains the helper functions
ImageDataset.py: dataset class
loss.py: DiceLoss class
unet.py: UNet model

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