Update README.md

README.md

-# Compairing performances across multiple Keras models by training them and applying transfer learning.
+# Compairing performances across multiple Keras models by training them and applying transfer learning on 3D images datasets.
 
-Author: alexcla99  
+Author: aclausse
 Version: 2.0.0
 
 ### Folder content:
 
 ```
-+-+- ft_data/                          # The folder containing the fine tuning dataset
-| +--- coma/                           # The folder containing coma MRIs
-| +--- control/                        # The folder containing control MRIs
++-+- multi_train              # A folder containing scripts to test a model with cross configurations
+| +--- dataset.py             # An override of the original dataset.py file
+| +--- fine_tune_v2.py        # A new version of fine_tune.py file
+| +--- train_all.py           # A script to run both training and fine tuning with multiple configurations
+| +--- train_all_balance.py   # The same script as above with balancing the train dataset
+| +--- train_v2.py            # A new version of train.py file
 |
-+-+- train_data/                       # The folder containing the train dataset
-| +--- abnormal/                       # The folder containing abnormal MRIs
-| +--- control/                        # The folder containing control MRIs
++-+- models/                  # The folder containing available models
+| +--- LeNet17.py             # The LeNet17 model
 |
-+-+- models/                           # The folder containing available models
-| +--- LeNet17.py                      # The LeNet17 model
-|
-+--- results/                          # The folder containing the train, transfer learning and tests results
-+--- __init__.py                       # An empty file to make this directory being a Python library
-+--- dataset.py                        # The dataset loader
-+--- fine_tune.py                      # A script to apply fine tuning on a model
-+--- README.md                         # This file
-+--- requirements.txt                  # The Python libraries to be installed in order to run the project
-+--- Results.ipynb                     # The obtained results and a description on how the model has been fine-tuned
-+--- settings.json                     # The settings of the model and the train phase
-+--- test_trained_model.py             # A script to test a trained model
-+--- test_fine_tuned_model.py          # A script to test a fine_tuned model
-+--- tf_config.py                      # A script to configure TensorFlow
-+--- train.py                          # A script to train from scratch a model
-+--- utils.py                          # Some utils
++--- results/                 # The folder containing the train, transfer learning and tests results
++--- train_data/              # The folder containing the dataset for training from scratch
++--- ft_data/                 # The folder containing the dataset for fine tuning
++--- __init__.py              # An empty file to make this directory being a Python library
++--- dataset.py               # The dataset loader
++--- fine_tune.py             # A script to apply fine tuning on a model
++--- README.md                # This file
++--- requirements.txt         # The Python libraries to be installed in order to run the project
++--- settings.json            # The settings of the model and the train phase
++--- test_trained_model.py    # A script to test a trained model
++--- test_fine_tuned_model.py # A script to test a fine tuned model
++--- tf_config.py             # A script to configure TensorFlow
++--- train.py                 # A script to train from scratch a model
++--- utils.py                 # Some utils
 ```
 
 ### Usage:
@@ -70,12 +70,9 @@ python3 test_fine_tuned_model.py <model:str>
 # Example: python3 test_fine_tuned_model.py LeNet17
 ```
 
-### Obtained results:
-
-See the `Results.ipynb` file.
-
 ### Many thanks to:
 
-3D images classification: https://keras.io/examples/vision/3D_image_classification/
+[1] H. Zunair., "3D images classification from CT scans.", [keras.io](https://keras.io/examples/vision/3D_image_classification/), 2020.  
+[2] Zunair et al., "Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction", [arXiv](https://arxiv.org/pdf/2007.13224.pdf), 2020.  
 
-LeNet17: https://arxiv.org/pdf/2007.13224.pdf
+License: [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0).
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