Update README to accommodate beta release of vision package (#1231)

* initial draft for vision readme

* Update README.rst

README.rst

@@ -41,7 +41,7 @@
 .. raw:: html
 
    <h1 align="center">
-      Test Suites for Validating ML Models & Data
+      Testing and Validating ML Models & Data
    </h1>
 
 .. raw:: html
@@ -59,16 +59,35 @@ machine learning models and data with minimal effort. This includes
 checks related to various types of issues, such as model performance,
 data integrity, distribution mismatches, and more.
 
+
+🖼️ Computer Vision & 🔢 Tabular Support
+==========================================
+**This README refers to the Tabular version** of deepchecks.
+
+Check out the `Deepchecks for Computer Vision & Images subpackage <deepchecks/vision>`__ for more details about deepchecks for CV, currently in *beta release*.
+
+
 💻 Installation
 =================
 
+
 Using pip
 ----------
 
 .. code:: bash
 
    pip install deepchecks -U --user
 
+.. note::
+
+   To install deepchecks together with the **Computer Vision Submodule** that is currently in *beta release*, replace ``deepchecks`` with ``"deepchecks[vision]"`` as follows.
+
+   .. code:: bash
+   
+      pip install "deepchecks[vision]" -U --user
+   
+   
+
 Using conda
 ------------
 
@@ -83,7 +102,7 @@ Using conda
 Head over to the `Quickstart Notebook <https://docs.deepchecks.com/en/stable/
 examples/guides/quickstart_in_5_minutes.html?
 utm_source=github.com&utm_medium=referral&utm_campaign=readme&utm_content=try_it_out>`__
-and choose the  |binder badge image|  or the  |colab badge image|  to have it up and running, and to then apply it on your own data and models.
+and see deepchecks output on a built-in dataset, or run it yourself to apply it on your own data and models.
 
 
 📊 Usage Examples
@@ -98,7 +117,7 @@ Example for running a suite on given `datasets`_ and with a `supported model`_:
 
 .. code:: python
 
-   from deepchecks.suites import full_suite
+   from deepchecks.tabular.suites import full_suite
    suite = full_suite()
    suite.run(train_dataset=train_dataset, test_dataset=test_dataset, model=model)
 
@@ -150,7 +169,7 @@ can be found in our `API Reference`_.
 
 .. code:: python
 
-   from deepchecks.checks import TrainTestFeatureDrift
+   from deepchecks.tabular.checks import TrainTestFeatureDrift
    import pandas as pd
 
    train_df = pd.read_csv('train_data.csv')
@@ -210,7 +229,7 @@ return value. An example for adding a condition would be:
 
 .. code:: python
 
-   from deepchecks.checks import BoostingOverfit
+   from deepchecks.tabular.checks import BoostingOverfit
    BoostingOverfit().add_condition_test_score_percent_decline_not_greater_than(threshold=0.05)
 
 which will return a check failure when running it if there is a difference of
@@ -245,17 +264,19 @@ of checks and optional conditions.
 
 
 What Do You Need in Order to Start Validating?
-----------------------------------------------
+==============================================
 
 Environment
-~~~~~~~~~~~~
+-----------
 
 - The deepchecks package installed
 
 - JupyterLab or Jupyter Notebook
 
+
 Data / Model 
-~~~~~~~~~~~~
+------------
+
 
 Depending on your phase and what you wish to validate, you'll need a
 subset of the following:
@@ -269,14 +290,15 @@ subset of the following:
 
 -  A `supported model`_ (e.g. scikit-learn models, XGBoost, any model implementing the `predict` method in the required format)
 
+
 Supported Data Types
-~~~~~~~~~~~~~~~~~~~~
+--------------------
+
+The package currently supports tabular data and is in *beta release* for the computer vision submodule.
 
-Currently the package supports tabular data.
-Stay tuned for the upcoming Computer Vision release.
 
 When Should I Run Deepchecks
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+----------------------------
 
 Deepchecks validation accompanies you from the initial phase when you
 have only raw data, through the data splits, and to the final stage of