Update README.md for GDRNPP

README.md

@@ -11,7 +11,7 @@ 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, 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:
+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 is:
 
 ```
 python /gdrnpp_bop2022/preprocessing/preprocess_fruitbin.py --src_directory PATH_TO_SRC_DIRECTORY --dst_directory PATH_TO_DST_DIRECTORY --scenario SCENARIO
@@ -23,7 +23,7 @@ python /gdrnpp_bop2022/preprocessing/preprocess_fruitbin.py --src_directory PATH
 
 `_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](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:
+In order to use GT bbox in the case of FruitBin Benchmark, 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 is:
 ```
 python /gdrnpp_bop2022/preprocessing/generate_gt.py`.
 ```
@@ -35,7 +35,6 @@ 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.
 
 ### Model evaluation
@@ -51,6 +50,22 @@ python /gdrnpp_bop2022/core/gdrn_modeling/tools/fruitbin/eval_pose.py --path_dat
 - --symmetry - a boolean value of whether the fruit is symmetrical. In the Fruitbin dataset, only banana and pear are asymmetrical.
 
 
+### Docker
+To facilitate reprodibility, a docker contener is provided with GDRNPP for FruitBin dataset.
+
+1. Loading the docker image: 
+```
+sudo docker pull guillaume0477/6d_pose:gdrnpp_fruitbin
+```
+2. Creating a container:
+```
+xhost +
+
+sudo docker run -it --env="DISPLAY=$DISPLAY" --env="QT_X11_NO_MITSHM=1" --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" --env="XAUTHORITY=$XAUTH" --volume="$Path_to_dataset/Datasets/BOP_format:/gdrnpp_bop2022/datasets/BOP_DATASETS/fruitbin" --volume="$XAUTH:$XAUTH" --net=host --gpus all --privileged --runtime=nvidia --shm-size 48G  guillaume0477/6d_pose:gdrnpp_fruitbin
+```
+In the example above, there is a volume parameter with a path that copies the fruitbin dataset from the local computer to the container. The fruitbin dataset can be downloaded from this [link](https://dataset-dl.liris.cnrs.fr/fruitbin/). 
+
+
 
 # Original README of GDRNPP for BOP2022