docs\feat: #48 details on oxford pet experimemt

- slight more details on oxfor pet experiments

docs/source/contribution.rst

@@ -89,4 +89,5 @@ For setting up a local jupyter notebook, run the following (inside your venv):
 
     python -m ipykernel install --user --name=seg_tgce_env
     
-Then, open jupyter lab and select the created kernel.
+Then, open your preference tool (jupyter lab, vscode viewer, etc) 
+and select the created kernel.

docs/source/experiments.rst

@@ -16,6 +16,27 @@ The images are of different sizes and aspect ratios.
 The dataset is available at the following link: 
 https://www.robots.ox.ac.uk/~vgg/data/pets/
 
+In particular, we used an implementation of this dataset in a simplified and 
+easily retriable format, available at the following link:
+
+https://github.com/UN-GCPDS/python-gcpds.image_segmentation
+
+
+Scorers emulation
+=================
+
+On itself, the Oxford-IIIT Pet dataset contains the masks which reffer to the
+ground truth and not to labels from different annotators, which makes this 
+dataset non suitable for the original intention of the project. However, we
+used this dataset to emulate the scorers' behavior, by training previously a model 
+with a simple UNet architecture and then using this model to predict for being 
+disturbed in the last encoder layer for producing scorers with different lebels of
+agreement.
+
+.. image:: resources/oxford_pet_scorers_emulation.png
+  :width: 100%
+  :align: center
+  :alt: How the scorers emulated noisy annotatiosn for the Oxford-IIIT Pet dataset look.
 
 Crowd Seg Histopatological images
 =================================

docs/source/introduction.rst

@@ -13,6 +13,7 @@ correspond to different objects or parts of objects.
 .. image:: resources/usual-segmentation.png
   :width: 400
   :alt: An example on how image segmentation resulting labels look like.
+  :align: center
 
 
 What does multiple annotators mean?