update readme

README.md

@@ -13,8 +13,11 @@
 # </div> -->
 ---
 
+<div align="center">
 
-![](https://storage.googleapis.com/mle-courses-prod/users/61b6fa1ba83a7e37c8309756/private-files/ff27e200-e181-11f0-b179-8566ca0312de-Untitled_design_(3).png)
+<p align="center">
+    <img src="https://storage.googleapis.com/mle-courses-prod/users/61b6fa1ba83a7e37c8309756/private-files/ff27e200-e181-11f0-b179-8566ca0312de-Untitled_design_(3).png" width="400"/>
+</p>
 
 <h1 align="center">
 ProtonX OCR tool: Table Detector
@@ -24,8 +27,6 @@ ProtonX OCR tool: Table Detector
 [![HuggingFace](https://img.shields.io/badge/HuggingFace-Model-black?logo=huggingface)](https://huggingface.co/protonx-models/protonx-tc)
 [![Website](https://img.shields.io/badge/protonx.co-Website-blue)](https://protonx.co)
 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1V9B38kbQP17RR0-WqVcPt0R7C5RiZ1_x?usp=sharing)
-
-
 ## **Introduction**
 
 This model is a **binary image classification model** designed to determine **whether an input document image contains at least one table**.
@@ -131,7 +132,9 @@ import torch
 import torchvision
 from protonx import ProtonX
 
-client = ProtonX()
+client = ProtonX(
+    mode="offline"
+)
 prediction = client.ocr.detect_table(image_path="images/document_page_01.png")
 
 print(prediction)