Updated gitlab and readme

.gitlab-ci.yml

 # run the test suite
 tests:
-    image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/multiview-machine-learning-omis/ubuntu:18.04
+    image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/summit/ubuntu:18.04
     tags:
         - docker
     script:
@@ -9,7 +9,7 @@ tests:
 
 # generate the documentation
 doc:
-    image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/multiview-machine-learning-omis/ubuntu:18.04
+    image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/summit/ubuntu:18.04
     tags:
         - docker
     only:
@@ -30,7 +30,7 @@ doc:
 
 # project public
 pages:
-    image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/multiview-machine-learning-omis/ubuntu:18.04
+    image: registry.gitlab.lis-lab.fr:5005/baptiste.bauvin/summit/ubuntu:18.04
     tags:
         - docker
     only:

README.md

@@ -71,6 +71,7 @@ For further information about classifier-specific arguments, see the [documentat
 In order to start a benchmark on your own dataset, you need to format it so SuMMIT can use it. 
 
 ##### If you already have an HDF5 dataset file it must be formatted as : 
+
 * One dataset for each view called `ViewI` with `I` being the view index with 2 attribures : 
     * `attrs["name"]` a string for the name of the view
     * `attrs["sparse"]` a boolean specifying whether the view is sparse or not (WIP)
@@ -79,6 +80,7 @@ In order to start a benchmark on your own dataset, you need to format it so SuMM
 * One dataset for the labels called `Labels` with one attribute : 
     * `attrs["names"]` a list of strings encoded in utf-8 naming the labels in the right order
 
+
 * One group for the additional data called `Metadata` containing at least 1 dataset :
     * `"example_ids"`, a numpy array of type `S100`, with the ids of the examples in the right order
 * And three attributes :