diff --git a/python/README.md b/python/README.md
index 1157a28..2011949 100644
--- a/python/README.md
+++ b/python/README.md
@@ -1,6 +1,11 @@
 <div align="center">
-    <h1>VBR Development Kit</h1>    
-
+    <h1>VBR Development Kit</h1>
+    <a href=""><img src=https://github.com/rvp-group/vbr-devkit/actions/workflows/python.yml/badge.svg /></a>
+    <a href=""><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/vbr-devkit" /></a>
+    <a href=""><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/vbr-devkit" /></a>
+    <br />
+    <br />
+    <a href="https://github.com/rvp-group/vbr-devkit"><img src="https://github.com/rvp-group/vbr-devkit/assets/5305530/f1a8d22a-af1e-42d4-b296-d94021a980cf"/></a>   
 </div>
 This kit contains utilities to work on the VBR SLAM dataset
 
@@ -10,14 +15,107 @@ This kit contains utilities to work on the VBR SLAM dataset
 pip install vbr-devkit
 ```
 
-# Usage
-You will find here the list of available commands to interact with our dataset
+You can install autocompletion for our package by typing:
+
+```shell
+vbr --install-completion
+```
 
+you might need to restart the shell for the autocompletion to take effect.
+
+# Usage
 ## Download sequences
 
+You can list the available sequences you can download by typing:
+
+```shell
+vbr list
+```
+You should see something similar to this
+![list](https://github.com/rvp-group/vbr-devkit/assets/5305530/c195e5b0-c5ee-4abb-a7f5-2ce97474ac4f)
+
+After choosing your sequence, you can type
+
+```shell
+vbr download <sequence_name> <save_directory>
+```
+
+For instance, we could save `campus_train0` as follows:
+
+```shell
+vbr download campus_train0 ~/data/
+```
+**N.B.** The script will actually save the sequence at `<save_directory>/vbr_slam/<sequence_prefix>/<sequence_name>`. Moreover, by calling the previous command, we expect the following directory:
+```
+data
+  - vbr_slam
+    - campus
+      - campus_train0
+        - vbr_calib.yaml
+        - campus_train0_gt.txt
+        - campus_train0_00.bag
+        - campus_train0_01.bag
+        - campus_train0_02.bag
+        - campus_train0_03.bag
+        - campus_train0_04.bag                     
+```
+
+## Convert format
+
+The sequences are provided in ROS1 format. We offer a convenient tool to change representation if you prefer working on a different format.
+You can see the supported formats by typing:
+
+```shell
+vbr convert --help
+```
+
+To convert a bag or a sequence of bags, type:
 ```shell
-vbr_download --dataset <sequence_name> --save-dir <output_path>
+vbr convert <desired_format> <input_directory/input_bag> <output_directory>
 ```
 
-You can get the list of all the available sequences by using the `-h | --help` flag
+for instance, we could convert the `campus_train0` sequence to `kitti` format as follows:
+
+```shell
+vbr convert kitti ~/data/vbr_slam/campus/campus_train0/campus_train0_00.bag ~/data/campus_train0_00_kitti/
+```
+
+We can expect the following result:
+
+```
+data
+  - campus_train0_00_kitti
+    - camera_left
+      - timestamps.txt
+      - data
+        - 0000000000.png
+        - 0000000001.png
+        - ...
+    - camera_right
+      - timestamps.txt
+      - data
+        - 0000000000.png
+        - 0000000001.png
+        - ...
+    - ouster_points
+      - timestamps.txt
+      - data
+        - .dtype.pkl
+        - 0000000000.bin
+        - 0000000001.bin
+        - ...
+    - ... 
+```
+
+**N.B.** In KITTI format, point clouds are embedded in binary files that can be opened using `Numpy` and `pickle` as follows:
+
+```python
+import numpy as np
+import pickle
+
+with open("campus_train0_00_kitti/ouster_points/data/.dtype.pkl", "rb") as f:
+    cdtype = pickle.load(f)
+
+cloud_numpy = np.fromfile("/campus_train0_00_kitti/ouster_points/data/0000000000.bin", dtype=cdtype)
+```