update readme

README.md

 # TODO: Add banner
-![Banner image]()
+![Banner image](https://images.aicrowd.com/uploads/ckeditor/pictures/1040/content_Desktop_Banner.png)
 
 # **[Music Demixing Challenge 2023 - Robust Music Separation](https://www.aicrowd.com/challenges/music-demixing-challenge-2023/problems/robust-music-separation)** - Starter Kit
 [![Discord](https://img.shields.io/discord/565639094860775436.svg)](https://discord.gg/fNRrSvZkry)
@@ -38,6 +38,8 @@ The Music Demixing Challenge 2023 (MDX23) is an opportunity for researchers and
 
 Given an **audio signal as input** (referred to as a "mixture"), you must **decompose in its different parts**. 
 
+![separation image](https://images.aicrowd.com/uploads/ckeditor/pictures/401/content_image.png)
+
 🎻 ROBUST MUSIC SEPARATION
 
 This task will focus on music source separation. Participants will submit systems that separate a song into four instruments: vocals, bass, drums, and other (the instrument "other" contains signals of all instruments other than the first three, e.g., guitar or piano). 
@@ -73,7 +75,9 @@ for each song. Finally, the overall score is obtained by averaging SDRsong over
 
 # Baselines
 
-We use Open-Unmix for the baseline. Specifically, we provide trained checkpoints for the UMXL model. You can use the baseline by switching to the openunmix-baseline branch on this repository. To test the models locally, you need to install `git-lfs`.
+We use the [Open-Unmix](https://github.com/sigsep/open-unmix-pytorch) library for the baseline. Specifically, we provide trained checkpoints for the UMXL model. You can use the baseline by switching to the `openunmix-baseline` branch on this repository. To test the models locally, you need to install `git-lfs`.
+
+When submitting your own models, you need to submit the checkpoints using `git-lfs`. Check the instructions shared in the inference file [here](https://gitlab.aicrowd.com/music-demixing-challenge-2023/mdx-2023-robust-music-separation-starter-kit/-/blob/openunmix-baseline/my_submission/openunmix_separation_model.py)
 
 # How to Test and Debug Locally
 
@@ -100,9 +104,7 @@ of the competition.
 
 # Dataset
 
-Download the public dataset for this Task using the link below, you'll need to accept the rules of the competition to access the data. The data is same as the well known MUSDB18-HQ dataset and its compressed version.
-
-https://www.aicrowd.com/challenges/music-demixing-challenge-2023/problems/robust-music-separation/dataset_files
+Download the public dataset for this task using this [link](https://www.aicrowd.com/challenges/music-demixing-challenge-2023/problems/robust-music-separation/dataset_files), you'll need to accept the rules of the competition to access the data. The data is same as the well known MUSDB18-HQ dataset and its compressed version.
 
 
 # Setting Up Your Codebase