Clean-up README

README.md

@@ -4,7 +4,7 @@
   </a>
 </p>
 
-# CAREamics Restoration
+# CAREamics
 
 [![License](https://img.shields.io/pypi/l/careamics.svg?color=green)](https://github.com/CAREamics/careamics/blob/main/LICENSE)
 [![PyPI](https://img.shields.io/pypi/v/careamics.svg?color=green)](https://pypi.org/project/careamics)
@@ -17,62 +17,11 @@
 ``` bash
 pip install careamics
 ```
-For more details on the options please follow the installation [guide](https://careamics.github.io/careamics/).
+For more details on the options please follow the installation [guide](https://careamics.github.io/installation/).
 
-## Usage
+<!-- 
+## Contributing
 
-CAREamics uses the Engine object to construct the pipeline for both training and prediction. First we import the Engine.
-```python
-from careamics_restoration.engine import Engine
-```
-The Engine could be initialized in 2 ways:
-1. Using the [yaml config](examples/n2v_2D_reference.yml) file
-
-Specify the mandatory parameters in the config file
-```yaml
-experiment_name: Name of the experiment
-working_directory: Path to the working directory, where all the outputs will be stored
-
-algorithm: 
-    loss: type of loss function, e.g. n2v for Noise2Void
-    model: model architecture, e.g. UNet
-    is_3D: True if 3D data, False if 2D data
-
-training:
-  num_epochs: Number of training epochs
-  patch_size: Size of the patches, List of 2 or 3 elements
-  batch_size: Batch size for training
-
-extraction_strategy: Controls how the patches are extracted from the data
-
-data:
-    data_format: File extension, e.g. tif
-    axes: Defines the shape of the input data
-```
-Full description of the configuration parameters is in the [documentation](https://careamics.github.io/careamics/).
-
-
-```python
-engine = Engine(config_path="config.yml")
-
-```
-2. Using the path to the pretrained model
-It's also possible to initialize the Engine using the model checkpoint, saved during the training or downloaded from the [BioImage Model Zoo](https://bioimage.io/#/).
-Checkpoint must contain model_state_dict.
-Read more abount saving and loading models in the [documentation](https://careamics.github.io/careamics/).
-
-Once Engine is initialized, we can start training, providing the relative paths to train and validation data
-
-```python
-engine.train(train_path=train_path, val_path=val_path)
-```
-Training will run for the specified number of epochs and save the model checkpoint in the working directory.
-
-Prediction could be done directly after the training or by loading the pretrained model checkpoint.
-
-```python
-predictions = engine.predict(pred_path=predict_path)
-```
-
-For more examples please take a look at the [notebooks](examples).
+## Citing us 
 
+-->