diff --git a/README.rst b/README.rst
index eac87e7888678f5bed2d5897ee27943eaca38eb7..62f754a77ffc1b59751aaf572b92bf1af027494f 100644
--- a/README.rst
+++ b/README.rst
@@ -95,9 +95,26 @@ Prepare folder configuration
     # Folder containing Multi-PIE face annotations
     bob config set bob.db.multipie.annotations_directory <path_to_folder>
     # Path to the Pickle file where to store computed latent directions
-    bob config set bob.synface.latent_directions <path_to_folder>
+    bob config set bob.synface.latent_directions <path_to_pickle_file.pkl>
 
 
+Run dataset projection
+**********************
+Database projection is performed by running the `./bin/project_db.py` script. Premade configuration files are available in the repository to 
+perform the projection of three subsets of interest of the Multipie database (image from the world group for the U, E and P protocol).
+StyleGAN2 only runs on GPU, therefore the projection should be made on a computation node with a GPU. Moreover, on the Idiap SGE grid, one can easily split the projection process
+between several parallel jobs by submitting them through `jman`. By default (unless one uses the `--force` flag), images are not reprojected if their latent projection is already
+present in the output folder, which enables to interrupt & restart projection in a series of sequential `sgpu` jobs.
+One can run `./bin/project_db.py --help` to get more info on the required configuration values.
+
+Example command:
+::
+
+   jman submit -n multipie_proj -q sgpu -t 8 -r 10 -s "PYTHONUNBUFFERED=1" -- ./bin/project_db.py multipie_P --checkpoint
+
+This commands launch 8 parallel projection jobs of the Multipie P protocol on the short gpu queue. As it uses the `--checkpoint` flag, not only latents projection will be saved, but also 
+the cropped images as well as the resynthetized StyleGAN2 images for each computed latent. This launch instructions is then repeated 10 times, until there are no more images left to project.
+ 
 Contact
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