Update README.md

README.md

@@ -25,14 +25,14 @@ We have released the code and arXiv preprint for our new project [6-PACK](https:
 
 ## Getting started for FruitBin 
 
-To clone the repository do the commands : 
+To clone the repository, the commands is : 
 
 ```
 git clone https://gitlab.liris.cnrs.fr/gduret/DenseFusion
 git checkout jz
 ```
 
-If the dataset is download from https://datasets.liris.cnrs.fr/fruitbin-version1, then the splitting of the data is done, a rearaangment will have to be done folowing these constrains : 
+If you have downloaded the dataset from https://datasets.liris.cnrs.fr/fruitbin-version1, the data splitting has already been performed. However, you will need to rearrange the data according to the following constraints:
 
 ```
 RGB_Resised -> rgb
@@ -49,7 +49,7 @@ pear2 -> 08
 
 ```
 
-Before considering the training, the data have to be arranged in a specific way. Following the steps described in the link : (https://gitlab.liris.cnrs.fr/gduret/fruitbin) with the command  :
+Before proceeding with the training, it is necessary to arrange the data in a specific manner. You can follow the steps outlined in the following link: (https://gitlab.liris.cnrs.fr/gduret/fruitbin) using the provided command:
 
 ```
 python3 compute_label.py --path_dataset=/gpfsscratch/rech/uli/ubn15wo/FruitBin1/FruitBin_low_1_0.7_1.0/ --target_folder=Generated_Cameras --path_DF_data=/gpfsscratch/rech/uli/ubn15wo/DenseFusion01_Cameras/datasets/linemod/Linemod_preprocessed/data --occ_data=""
@@ -99,24 +99,22 @@ The expect folders result architectures is :
 │   │   │   └── models
 ```
 
-If the processed has been successful, the training command is :
+If the preprocessing has been successful, you can proceed with the training by using the following command:
 
 ```
 bash ./experiments/scripts/train_linemod.sh
 ```
 
+To assess the model's performance, the segmentation results need to be saved in the designated folder. To accomplish this, you can utilize the "densefusion_gt_to_segnet.sh" script available in the FruitBin repository: https://gitlab.liris.cnrs.fr/gduret/fruitbin.
 
-To evaluate the model, segmentation result have to be saved in the folder : 
+You can execute the command as follows:
 
-To do so, it can be used the script densefusion_gt_to_segnet.sh from the repo Fruibin : https://gitlab.liris.cnrs.fr/gduret/fruitbin
-
-The command can run as follow : 
 
 ```
 bash densefusion_gt_to_segnet.sh /gpfsscratch/rech/uli/ubn15wo/FruitBin1/FruitBin_low_1_0.7_1.0/Generated_Cameras_Evaluating /gpfsscratch/rech/uli/ubn15wo/DenseFusion/datasets/linemod/Linemod_preprocessed/segnet_results
 ```
 
-The expected result architecture is (only label_gt is used) :
+The desired result architecture consists of the following components (with only "label_gt" being utilized):
 
 ```
 ├── datasets
@@ -151,9 +149,7 @@ The evaluation command is :
 bash ./experiments/scripts/eval_linemod.sh
 ```
 
-eval_linemod.sh have to be modified to select the corresponding trained model. 
-
-
+To select the appropriate trained model, you will need to make modifications to the eval_linemod.sh script.
 
 ## Overview