Update file README.md

README.md

@@ -19,15 +19,15 @@ python /gdrnpp_bop2022/preprocessing/preprocess_fruitbin.py --src_directory PATH
 
 - --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:
+- --scenario - the scenario for splitting data in the dataset. 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`
 
-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:
+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](https://gitlab.liris.cnrs.fr/gduret/gdrnpp_bop2022/-/blob/main/preprocessing/generate_gt.py?ref_type=heads) 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`.
 ```
-After creating the file, make sure that the path to it is correct in the main config file
+After creating the file, make sure that the path to it is correct in the main config file in GDRNPP.
 
 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:
 ```
@@ -46,7 +46,7 @@ python /gdrnpp_bop2022/core/gdrn_modeling/tools/fruitbin/eval_pose.py --path_dat
 ```
 
 - --path_data - path to the dataset
-- --pred_path - the path to the .csv file created after testing the model with the detected positions
+- --pred_path - path to .csv file with detected positions created after testing the model
 - --class_name - the fruit for which the pose accuracy is estimated 
 - --symmetry - a boolean value of whether the fruit is symmetrical. In the Fruitbin dataset, only banana and pear are asymmetrical.