[ADD] info about model architecture

README.md

@@ -43,14 +43,18 @@ Then, follow the pipeline!
 ```bash
 python3 dataset.py
 ```
-This will create a `tiles` folder containing the tiles of the processed satellite images. The path to the processed satellite images that are to be processed should be updated in the `config.yaml` file.
+Because the satellite images are too big to be processed efficiently by `detectron2` we split the images into `tiles`. **Tile size** can be changed in the `config.yaml` file and can slighlty change the results (we recommand to do a `hyperparameter search` to search for the optimal tile size).
+
+The script will create a `tiles` folder containing the tiles of the processed satellite images. The path to the processed satellite images that are to be processed should be updated in the `config.yaml` file.
 
 ## Train detectron2
 
 ```bash
 python3 train.py
 ```
 
+With this script we train a `Detectron2` model with a [faster-rcnn architecture](https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml) as backbone. Note that it is possible to change the backbone by choosing another [Detectron compliant](https://github.com/facebookresearch/detectron2/tree/main/configs/COCO-Detection) model.
+
 The script should create a folder `output` that contains `model_final.pth`, the logs and other files that `Detectron2` creates.
 
 ## Predict