added license and updated readme

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Image Processing Group - BarcelonaTECH - UPC
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,11 +1,146 @@
 # CartNet: Cartesian Encoding for Anisotropic Displacement Parameters Estimation
 
-![Description of the figure](./fig/pipeline.png)
+![Pipeline](./fig/pipeline.png)
 
+[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
 
-## Setting Up the Environment
+## Overview
 
-To create the Conda environment for this project, use the following command:
+Implementation of the CartNet model proposed in the paper:
+
+- **Paper**: [CartNet: Cartesian Encoding for Anisotropic Displacement Parameters Estimation](link_to_paper)
+- **Authors**: Àlex Solé, Albert Mosella-Montoro, Joan Cardona, Silvia Gómez-Coca, Daniel Aravena, Eliseo Ruiz and Javier Ruiz-Hidalgo
+- **Conference/Journal**: [Digital Discovery](https://www.rsc.org/journals-books-databases/about-journals/digital-discovery/), Year
+
+## Table of Contents
+
+- [Installation](#installation)
+- [Dependencies](#dependencies)
+- [Dataset](#dataset)
+- [Training](#training)
+- [Results](#results)
+- [Pre-trained Models](#pre-trained-models)
+- [Citation](#citation)
+- [License](#license)
+- [Acknowledgments](#acknowledgments)
+
+
+## Installation
+
+Instructions to set up the environment:
+
+```sh
+# Clone the repository
+git clone https://github.com/imatge-upc/CartNet.git
+cd CartNet
+
+# Create a Conda environment
+conda env create -f environment.yml
+
+# Activate the environment
+conda activate CartNet
+```
+
+## Dependencies
+
+The environment relies on these dependencies:
+
+```sh
+pytorch==1.13.1
+pytorch-cuda==11.7
+pyg==2.5.2
+pytorch-scatter==2.1.1
+scikit-learn==1.5.1
+scipy==1.13.1
+pandas==2.2.2
+wandb==0.17.3
+yacs==0.1.6
+jarvis-tools==2024.8.30
+lightning==2.2.5
+roma==1.5.0
+e3nn==0.5.1
+```
+
+These dependencies are automatically installed when you create the Conda environment using the `environment.yml` file.
+
+
+## Dataset
+
+## Training
+
+To recreate the experiments from the paper:
+
+
+### ADP:
+
+To train **ADP Dataset** using **CartNet**:
+
+```sh
+bash train_scripts/train_cartnet_adp.sh
+```
+
+To train **ADP Dataset** using **eComformer**:
 
 ```sh
-conda env create -f environment.yml
+bash train_scripts/train_ecomformer_adp.sh
+```
+To train **ADP Dataset** using **eComformer**:
+
+```sh
+bash train_scripts/train_icomformer_adp.sh
+```
+
+## Evaluation
+
+Instructions to evaluate the model:
+
+```sh
+# Command to evaluate the model
+python evaluate.py --config configs/eval_config.yaml --checkpoint path/to/checkpoint.pth
+```
+
+## Results
+
+Include quantitative results, such as accuracy, and qualitative results, like sample outputs:
+
+| Metric   | Value |
+| -------- | ----- |
+| Accuracy | 95%   |
+| F1 Score | 0.94  |
+| ...      | ...   |
+
+## Pre-trained Models
+
+Links to download pre-trained models:
+
+- [Model Name](link_to_model) (e.g., Google Drive, AWS S3)
+
+
+## Citation
+
+If you use this code in your research, please cite:
+
+```bibtex
+@article{your_paper_citation,
+  title={Title of the Paper},
+  author={Author1 and Author2 and Author3},
+  journal={Journal Name},
+  year={2023},
+  volume={XX},
+  number={YY},
+  pages={ZZZ}
+}
+```
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Acknowledgments
+
+- Mention any collaborators or funding sources.
+- Credit libraries or resources that were helpful.
+
+
+
+

train_scripts/train_icomformer_adp.sh

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+CUDA_VISIBLE_DEVICES=0 python main.py --seed 0 --name "CartNet" --model "CartNet" --dataset "ADP" --dataset_path "/scratch/g1alexs/ADP_DATASET"  \
+                                    --wandb_project "CartNet Paper" --batch_size 64 --lr 0.001 --epochs 50 \
+                                    --augment & 
+CUDA_VISIBLE_DEVICES=4 python main.py --seed 1 --name "CartNet" --model "CartNet" --dataset "ADP" --dataset_path "/scratch/g1alexs/ADP_DATASET" \
+                                    --wandb_project "CartNet Paper" --batch_size 64 --lr 0.001 --epochs 50 \
+                                    --augment & 
+
+CUDA_VISIBLE_DEVICES=2 python main.py --seed 2 --name "CartNet" --model "CartNet" --dataset "ADP" --dataset_path "/scratch/g1alexs/ADP_DATASET" \
+                                    --wandb_project "CartNet Paper" --batch_size 64 --lr 0.001 --epochs 50 \
+                                    --augment & 
+
+CUDA_VISIBLE_DEVICES=3 python main.py --seed 3 --name "CartNet" --model "CartNet" --dataset "ADP" --dataset_path "/scratch/g1alexs/ADP_DATASET" \
+                                    --wandb_project "CartNet Paper" --batch_size 64 --lr 0.001 --epochs 50 \
+                                    --augment &