diff --git a/README.md b/README.md
index e986abb..d16966d 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,22 @@
 # Collaborative-Coding-Exam
 Repository for final evaluation in the FYS-8805 Reproducible Research and Collaborative coding course
 
+## **Table of Contents**  
+1. [Project Description](#project-description)  
+2. [Installation](#installation)  
+3. [Usage](#usage)  
+4. [Results](#results)  
+5. [Citing](#citing)  
+
+## Project Description
+This project involves collaborative work on a digit classification task, where each participant works on distinct but interconnected components within a shared codebase. <br>
+The main goal is to develop and train digit classification models collaboratively, with a focus on leveraging shared resources and learning efficient experimentation practices.
+### Key Aspects of the Project:
+- **Individual and Joint Tasks:** Each participant has separate tasks, such as implementing a digit classification dataset, a neural network model, and an evaluation metric. However, all models and datasets must be compatible, as we can only train and evaluate using partners' models and datasets.
+- **Shared Environment:** Alongside working on our individual tasks, we collaborate on joint tasks like the main file, and training and evaluation loops. Additionally, we utilize a shared Weights and Biases environment for experiment management.
+- **Documentation and Package Management:** To ensure proper documentation and ease of use, we set up Sphinx documentation and made the repository pip-installable
+- **High-Performance Computing:** A key learning objective of this project is to gain experience with running experiments on high-performance computing (HPC) resources. To this end, we trained all models on a cluster
+
 ## Installation
 
 Install from:
@@ -25,9 +41,37 @@ python -c "import CollaborativeCoding"
 
 ## Usage
 
-TODO: Fill in
+To train a classification model using this code, follow these steps:
+
+### 1) Create a Directory for the reuslts
+Before running the training script, ensure the results directory exists:
+
+ `mkdir -p "<RESULTS_DIRECTORY>"`
+
+### 2) Run the following command for training, evaluation and testing
+
+ `python3 main.py --modelname "<MODEL_NAME>" --dataset "<DATASET_NAME>" --metric "<METRIC_1>" "<METRIC_2>" ... "<METRIC_N>" --resultfolder "<RESULTS_DIRECTORY>" --run_name "<RUN_NAME>" --device "<DEVICE>"`
+<br> Replace placeholders with your desired values:
+
+- `<MODEL_NAME>`: You can choose from different models ( `"MagnusModel", "ChristianModel", "SolveigModel", "JanModel", "JohanModel"`).
+
+
+- `<DATASET_NAME>`: The following datasets are supported (`"svhn", "usps_0-6", "usps_7-9", "mnist_0-3", "mnist_4-9"`)
+
+
+- `<METRIC_1> ... <METRIC_N>`: Specify one or more evaluation metrics (`"entropy", "f1", "recall", "precision", "accuracy"`)
+
+
+- `<RESULTS_DIRECTORY>`: Folder where all model outputs, logs, and checkpoints are saved 
+
+
+- `<RUN_NAME>`: Name for WANDB project
+
+
+- `<DEVICE>`: `"cuda", "cpu", "mps"`
+
 
-### Running on a k8s cluster
+## Running on a k8s cluster
 
 In your job manifest, include:
 
@@ -48,8 +92,8 @@ to pull the latest build, or check the [packages](https://github.com/SFI-Visual-
 > The container is build for a `linux/amd64` architecture to properly build Cuda 12. For other architectures please build the docker image locally.
 
 
-# Results 
-## JanModel & MNIST_0-3
+## Results 
+### JanModel & MNIST_0-3
 This section reports the results from using the model "JanModel" and the dataset MNIST_0-3 which contains MNIST digits from 0 to 3 (Four classes total). 
 For this experiment we use all five available metrics, and train for a total of 20 epochs.
 
@@ -62,7 +106,7 @@ We achieve a great fit on the data. Below are the results for the described run:
 | Test          | 0.024 | 0.004   | 0.994    | 0.994     | 0.994  | 0.994 |
 
 
-## MagnusModel & SVHN 
+### MagnusModel & SVHN 
 The MagnusModel was trained on the SVHN dataset, utilizing all five metrics.   
 Employing micro-averaging for the calculation of F1 score, accuracy, recall, and precision, the model was fine-tuned over 20 epochs.   
 A learning rate of 0.001 and a batch size of 64 were selected to optimize the training process.