diff --git a/doc/baselines.rst b/doc/baselines.rst
index 4f8d9c2da0043f74340d937ba66169613d803863..3913fa50404e0bdc334e0923aa7f48504c9c88e4 100644
--- a/doc/baselines.rst
+++ b/doc/baselines.rst
@@ -55,20 +55,20 @@ Deep learning baselines
 
 * ``inception-resnetv1-casiawebface``: Inception Resnet v1 model trained using the Casia Web dataset in the context of the work published by [TFP18]_
 
-* ``arcface-insightface``: Arcface model from `Insightface <https://github.com/deepinsight/insightface>`_
+* ``arcface-insightface``: Arcface model (Resnet100 backbone) from `Insightface <https://github.com/deepinsight/insightface>`_
 
+* ``resnet50-msceleb-arcface-2021``: Resnet Arcface model trained with MSCeleb dataset (dataset partially prunned)
 
-Deep Learning with different interfaces baselines
-=================================================
+* ``resnet50-msceleb-arcface-20210521``: Arcface model trained with MSCeleb dataset (dataset prunned)
 
-* ``mxnet-pipe``: Arcface Resnet Model using MxNet Interfaces from `Insightface <https://github.com/deepinsight/insightface>`_
+* ``resnet50-vgg2-arcface-2021``: Arcface model trained with VGG2 dataset 
 
-* ``mxnet-tinyface``: Applying `tinyface annoator <https://github.com/chinakook/hr101_mxnet>`_ for the Arcface Resnet Model using MxNet Interfaces from `Insightface <https://github.com/deepinsight/insightface>`_
+* ``iresnet34``: Arcface model (Resnet 34 backbone) from `Pytorch InsightFace <https://github.com/nizhib/pytorch-insightface>`_
+  
+* ``iresnet50``: Arcface model (Resnet 50 backbone) from `Pytorch InsightFace <https://github.com/nizhib/pytorch-insightface>`_
+  
+* ``iresnet100``: Arcface model (Resnet 100 backbone) from `Pytorch InsightFace <https://github.com/nizhib/pytorch-insightface>`_
 
-* ``pytorch-pipe-v1``: Pytorch network that extracts 1000-dimensional features, trained by Manual Gunther, as described in [LGB18]_
+* ``vgg16-oxford``: VGG16 Face model from `Oxford <https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/>`_
 
-* ``pytorch-pipe-v2``: Inception Resnet face recognition model from `facenet_pytorch <https://github.com/timesler/facenet-pytorch>`_
-
-* ``tf-pipe``: Inception Resnet v2 model trained using the MSCeleb dataset in the context of the work published by [TFP18]_
-
-* ``opencv-pipe``: VGG Face descriptor pretrained models, i.e. `Caffe model <https://www.robots.ox.ac.uk/~vgg/software/vgg_face/>`_
+* ``afffe``: Pytorch network that extracts 1000-dimensional features, trained by Manual Gunther, as described in [LGB18]_
diff --git a/doc/leaderboard/mobio.rst b/doc/leaderboard/mobio.rst
index 708566505b5b666b590f415f0b2f2545b25048b4..0d92490d2a8e14143cebf9ebf07f852c83597b4e 100644
--- a/doc/leaderboard/mobio.rst
+++ b/doc/leaderboard/mobio.rst
@@ -7,7 +7,52 @@ Mobio Dataset
 =============
 
 
-.. todo::
-   Benchmarks on Mobio Database
+The MOBIO dataset is a video database containing bimodal data (face/speaker).
+It is composed by 152 people (split in the two genders male and female), mostly Europeans, split in 5 sessions (few weeks time lapse between sessions).
+The database was recorded using two types of mobile devices: mobile phones (NOKIA N93i) and laptop 
+computers(standard 2008 MacBook).
+
+For face recognition images are used instead of videos.
+One image was extracted from each video by choosing the video frame after 10 seconds.
+The eye positions were manually labelled and distributed with the database.
+
+For more information check:
+
+.. code-block:: latex
+
+    @article{McCool_IET_BMT_2013,
+        title = {Session variability modelling for face authentication},
+        author = {McCool, Chris and Wallace, Roy and McLaren, Mitchell and El Shafey, Laurent and Marcel, S{\'{e}}bastien},
+        month = sep,
+        journal = {IET Biometrics},
+        volume = {2},
+        number = {3},
+        year = {2013},
+        pages = {117-129},
+        issn = {2047-4938},
+        doi = {10.1049/iet-bmt.2012.0059},
+    }
+
+
+Benchmarks
+==========
+    
+You can run the mobio baselines command with a simple command such as:
+
+.. code-block:: bash
+
+   bob bio pipeline vanilla-biometrics mobio-male arcface-insightface
+
+
+Scores from some of our baselines can be found `here <https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/scores/mobio-male.tar.gz>`_.
+A det curve can be generated with these scores by running the following commands:
+
+.. code-block:: bash
+
+   wget https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/scores/mobio-male.tar.gz   
+   tar -xzvf mobio-male.tar.gz
+   bob bio det ./mobio-male/{arcface_insightFace_lresnet100,inception_resnet_v2_msceleb_centerloss_2018,iresnet50,iresnet100,mobilenetv2_msceleb_arcface_2021,resnet50_msceleb_arcface_20210521,vgg16_oxford_baseline,afffe_baseline}/scores-{dev,eval} --legends arcface_insightFace_lresnet100,inception_resnet_v2_msceleb_centerloss_2018,iresnet50,iresnet100,mobilenetv2_msceleb_arcface_2021,resnet50_msceleb_arcface_20210521,vgg16_oxford_baseline,afffe -S -e --figsize 16,8
+
+and get the following :download:`plot <./plots/det-mobio-male.pdf>`.
+
 
-   Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/plots/det-mobio-male.pdf b/doc/leaderboard/plots/det-mobio-male.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..f2794c3965c31ae300e02c3381e91d30e2a2f583
Binary files /dev/null and b/doc/leaderboard/plots/det-mobio-male.pdf differ