/ml-project-2-las_macinas

ml-project-2-las_macinas created by GitHub Classroom

Primary LanguageJupyter Notebook

Machine Learning Project 2 - Road Segmentation

For the EPFL course Machine Learning CS-433, we developed a binary classifier to identify roads in satellite images and distinguish them from the background. We compared various CNN and U-Net architectures to determine the best model for this task. Our approach also included image pre-processing steps to improve the accuracy of the classifier. Our results showed that the U-Net model achieved an F-1 score of 0.903. This project demonstrates the effectiveness of using U-Net model for road segmentation in satellite imagery.

The structure of the image dataset and their groundtruth can be seen in the following image:

Alt text

Tutorial

To run with our weights saved and our testing dataset cleaned and augmented:

  1. Download train an test sets link and put them in the data file
  2. Just launch "run.py"

To train and run the model to have predictions:

  1. Download train an test sets link and put them in the data file
  2. Create a folder named models and another folder submission
  3. In "constants.py", set the TRAINING_MODE to True and TESTING_MODE to True
  4. Launch "run.py"
  5. The desired predictions will be in /submission/submission.csv

Structure

.  
├── data                    # Dataset for training/ testing the model  
├── report                  # Report of the project  
├── src                     # Source files
│    └── submissions        # Folder containing the prediction
│    └── models             # Savestate of the best models    
│    └── run.py             # Run the model : training and testing
└── README.md

Dataset

To run the model or to train it, the data has to be structured in a specific way with respect to the root directory where the 'run.py' file is situated.

.  
├── ...  
├── data  
│   ├── training  
│           └── groundtruth         # Groundtruth of the training images (400x400)  
│           └── images              # Training images (400x400 RGB)  
│   └── test                        # Test images (608x608 RGB)  
└── ...

Model used

Net

Run the code

In order to run the best model that was implemented the following command has to be sent in a terminal:

python run.py

This has to be done with the data organised as mentioned above.

Results

With the optimal model, one of the Unet versions the following results are obtained:

Validation F1-score Validation accuracy Test F1-score Test Accuracy
U-Net 0.945 ----- 0.903 0.948

The prediction on an image is :

Alt text

Authors - LAS MACINAS