Update README.md

README.md

@@ -44,16 +44,18 @@ size_categories:
 
 # Soup Can Object Detection Dataset Sample
 
-## [Duality.ai](https://www.duality.ai/edu) just released a 1000 image dataset used to train a YOLOv8 model for object detection -- and it's 100% free!
+ [Duality.ai](https://www.duality.ai/edu) just released a 1000 image dataset used to train a YOLOv8 model for object detection -- and it's 100% free!
 
-## Just [create an EDU account here](https://falcon.duality.ai/secure/documentation/ex2-dataset?sidebarMode=learn&highlight=dataset&utm_source=huggingface&utm_medium=dataset&utm_campaign=soupCan). 
+ Just [create an EDU account here](https://falcon.duality.ai/secure/documentation/ex2-dataset?sidebarMode=learn&highlight=dataset&utm_source=huggingface&utm_medium=dataset&utm_campaign=soupCan). 
 
 This HuggingFace dataset is a 20 image and label sample, but you can get the rest at no cost by [creating a FalconCloud account](https://falcon.duality.ai/secure/documentation/ex2-dataset?sidebarMode=learn&highlight=dataset&utm_source=huggingface&utm_medium=dataset&utm_campaign=soupCan). Once you verify your email, the link will redirect you to the dataset page.
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c8dc99951843ca6762fe02/ieA6xPKurg_zObz7lOk7N.png)
+
 # Dataset Overview
 This dataset consists of high-quality images of soup cans captured in various  poses and lighting conditions .This dataset is structured to train and test object detection models, specifically YOLO-based and other object detection frameworks.
 
-## Why Use This Dataset?
+#### Why Use This Dataset?
 - Single Object Detection: Specifically curated for detecting soup cans, making it ideal for fine-tuning models for retail, inventory management, or robotics applications.
 
 - Varied Environments: The dataset contains images with different lighting conditions, poses, and occlusions to help solve traditional recall problems in real world object detection.
@@ -78,28 +80,37 @@ Multiclass Object Detection Dataset/
 |   |-- ...
 ```
 
-Components
-Images: RGB images of the soup can in .png format.
-Labels: .txt files containing bounding box annotations in the YOLO format.
-0 = soup can
-Example Annotation (YOLO Format):
+### Components
 
-0 0.475 0.554 0.050 0.050
+Images: RGB images of the object in `.png` format.
 
-Where:
-0 represents the object class (soup can).
-The next four values represent the bounding box coordinates (normalized x_center, y_center, width, height).
-Usage
-This dataset is designed to be used with popular deep learning frameworks:
+Labels: Text files (`.txt`) containing bounding box annotations for each class:
+- 0 = soup
 
-from datasets import load_dataset
+### Example Annotation (YOLO Format):
 
-dataset = load_dataset("your-huggingface-username/Soup-Can-Object-Detection")
+```plaintext
+0 0.475 0.554 0.050 0.050
+```
 
-To train a YOLOv8 model, you can use Ultralytics' yolo package:
+- 0 represents the object class (soup can).
+- The next four values represent the bounding box coordinates (normalized x_center, y_center, width, height).
 
-yolo train model=yolov8n.pt data=soup_can.yaml epochs=50 imgsz=640
+### Usage
+This dataset is designed to be used with popular deep learning frameworks. Run these commands:
 
+```plaintext
+from datasets import load_dataset
+```
+```plaintext
+dataset = load_dataset("your-huggingface-username/YOLOv8-Object-Detection-02-Dataset")
+```
+ 
+To train a YOLOv8 model, you can use Ultralytics' yolo package:
+ 
+```plaintext
+yolo train model=yolov8n.pt data=soup_can.yaml epochs=50 imgsz=640
+```
 
 Licensing
 License: Apache 2.0