Update file README.md

README.md

@@ -11,28 +11,30 @@ In order to train the GDRNPP model on a new dataset, it is necessary to have thi
 
 The first step is to resize the bounding boxes. It is necessary to reduce them for correct operation. Here is the [script](https://gitlab.liris.cnrs.fr/gduret/gdrnpp_bop2022/-/blob/main/preprocessing/resize_bbox.py?ref_type=heads). The paths to the input and output folders are hardcoded and can be easily changed.
 
-Then, a [script](https://gitlab.liris.cnrs.fr/gduret/gdrnpp_bop2022/-/blob/main/preprocessing/preprocess_fruitbin.py?ref_type=heads) was created that does the main part of preprocessing. It creates the necessary directories, copies the necessary files into them, and creates json files with ground truth in the required format. The command to run the script:
+Then, it is necessary to use the [script](https://gitlab.liris.cnrs.fr/gduret/gdrnpp_bop2022/-/blob/main/preprocessing/preprocess_fruitbin.py?ref_type=heads) that does the main part of preprocessing. It creates the necessary directories, copies the necessary files into them, and creates json files with ground truth in the required format. The command to run the script:
 
-`python /gdrnpp_bop2022/preprocessing/preprocess_fruitbin.py --src_directory PATH_TO_SRC_DIRECTORY --dst_directory PATH_TO_DST_DIRECTORY --scenario SCENARIO`
+```
+python /gdrnpp_bop2022/preprocessing/preprocess_fruitbin.py --src_directory PATH_TO_SRC_DIRECTORY --dst_directory PATH_TO_DST_DIRECTORY --scenario SCENARIO
+```
 
 - --src_directory - the input directory with the folders of all the fruits;
 - --dst_directory - the output directory;
 - --scenario - the scenario for splitting data in the dataset from the Splitting folder. Basic dataset splitting scenarios:
-```
 
-_world_occ_07.txt, _world_occ_05.txt, _world_occ_03.txt, _world_occ_01.txt, _camera_occ_07.txt, _camera_occ_05.txt, _camera_occ_03.txt, _camera_occ_01.txt
+`_world_occ_07.txt, _world_occ_05.txt, _world_occ_03.txt, _world_occ_01.txt,  _camera_occ_07.txt, _camera_occ_05.txt, _camera_occ_03.txt, _camera_occ_01.txt`
 
-```
 Due to the specifics of the fruitbin dataset, it turned out that using yolox did not give good results, so instead of the detections detected by yolox, a gt was used. To do this, a script was written that generates a .json file of gt in the required format. The command to run the script:
-
-  `python /gdrnpp_bop2022/preprocessing/generate_gt.py`.
-
+```
+python /gdrnpp_bop2022/preprocessing/generate_gt.py`.
+```
 After creating the file, make sure that the path to it is correct in the main config file
 
 The [generate_image_sets_file](https://gitlab.liris.cnrs.fr/gduret/gdrnpp_bop2022/-/blob/main/preprocessing/generate_image_sets_file.py?ref_type=heads) and [generate_test_targets_file](https://gitlab.liris.cnrs.fr/gduret/gdrnpp_bop2022/-/blob/main/preprocessing/generate_test_targets_file.py?ref_type=heads) scripts create two files required for testing:
+```
+python /gdrnpp_bop2022/preprocessing/generate_image_sets_file.py
+python /gdrnpp_bop2022/preprocessing/generate_test_targets_file.py
+```
 
-`python /gdrnpp_bop2022/preprocessing/generate_image_sets_file.py`
-`python /gdrnpp_bop2022/preprocessing/generate_test_targets_file.py`
 
 The paths to the input and output directories are also hardcoded in the script. If necessary, the scenario for splitting data in the dataset can also be changed.