jtz18 / 50.039-deep-learning

50.039-DL

This repository contains the code for the 50.039-DL project.

Setup

Before running the code, please follow these setup instructions:

1. Run the setup.sh script. This script downloads a checkpoints.zip file and unzips it into a checkpoints folder. You can run the script with the following command:

./setup.sh

Make sure the script is executable. If it's not, you can make it executable with the following command:

chmod +x setup.sh

If the installation fails, you can manually download the checkpoints.zip file from this link and unzip it into a checkpoints folder in the root project directory on the same level as notebooks, sota, src folders.

2. Install the required Python dependencies. You can do this with the following command:

pip install -r requirements.txt

This command reads the requirements.txt file and installs all the listed packages.

After following these setup instructions, you should be ready to run the code.

Project Structure

This project is organised into several key directories and files:

• src/: This directory contains the models that we developed for this project. Specifically, it includes implementations of U-Net, U-NetR, and SegFormer models from scratch with reference to research papers for the architecture.

• sota/: This directory contains state-of-the-art models that we used for comparison with our models. Specifically, it includes an implementation of the TransFuse model.

• sweeps.ipynb: This Jupyter notebook contains the code for performing hyperparameter sweeps on our models. We used this notebook to find the best hyperparameters for each model.

• train.ipynb: This Jupyter notebook contains the code for training our models. After finding the best hyperparameters for each model, we used this notebook to train each model for 10 epochs.

• evaluation.ipynb: This Jupyter notebook contains the code for evaluating our models. After training the models, we used this notebook to compute the Dice score on the test set for each model.

Evaluation

We evaluated our models using the Dice score metric. This metric provides a measure of the overlap between the predicted and actual segmentations, with a higher score indicating better performance.

We compared the performance of our models with that of the state-of-the-art TransFuse model. This comparison allowed us to assess the effectiveness of our models in relation to existing methods.

Running the Code

To validate our test results, you can run the evaluation.ipynb Jupyter notebook. This notebook loads the trained weights of each model and computes the Dice Score on the test set.

Before running the notebook, make sure you have the checkpoints folder in your project directory. This folder should contain the trained weights for all the models. You can obtain this folder by running the setup.sh script as described in the Setup section.

Reproducing sweeps, training, and evaluation

Note that you will have to set up a WandB account and configure your CLI to run the codes correctly.

Sweeps

1. Go to notebooks/sweeps.ipynb
2. Scroll to the section on "Training Loop" to comment out the model of choice.
3. Scroll to the section on "Init Wandb" and change the number of run_cap in the configuration. For U-net: 43, UNETR: 65, SegFormer: 39.
4. Run the entire notebook and wait for the number of desired sweeps to complete.

Training

1. Go to notebooks/train.ipynb
2. Scroll to the section on "Training Loop" to comment out the model of choice. The models have already been pre-loaded with the best hyperparameters from the sweeps section.
3. Run the entire notebook and wait for 10 epochs to complete.

Evaluation

1. Go to notebooks/evaluation.ipynb
2. Download the weights from this link and unzip it into the notebooks/checkpoints folder.
3. Run the entire notebook.