Update README.md

README.md

@@ -41,38 +41,42 @@ tags:
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 - 1K<n<10K
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-Soup Can Object Detection Dataset Sample
-Duality.ai 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](link). 
-This HuggingFace dataset is a 20 image and label sample, but you can get the rest at no cost by [creating a FalconCloud account](link). Once you verify your email, the link will redirect you to the dataset page.
 
-What makes this dataset unique, useful, and capable of bridging the Sim2Real gap?
-- The digital twins are not generated by AI, but instead crafted by 3D artists to be INDISTINGUISHABLE to the model from the physical-world objects. This allows the training from this data to transfer into real-world applicability
-- The simulation software, called FalconEditor, can easily create thousands of images with varying lighting, posing, occlusions, backgrounds, camera positions, and more. This enables robust model training.
-- The labels are created along with the data. This not only saves large amounts of time, but also ensures the labels are incredibly accurate and reliable.
+# 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!
 
-Dataset Overview
+## 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.
+
+# 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?
-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.
+## 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.
 
-Accurate Annotations: Bounding box annotations are precise and automatically labeled in YOLO format as the data is created.
+- Accurate Annotations: Bounding box annotations are precise and automatically labeled in YOLO format as the data is created.
 Create your own specialized data!
 You can create a dataset like this but with your own digital twin! [Create an account and follow this tutorial to learn how](link).
-Dataset Structure
+
+# Dataset Structure
+
 The dataset is organized as follows:
-Soup-Can-Object-Detection-Dataset/
-│-- images/
-│   ├── 000000000.png
-│   ├── 000000001.png
-│   ├── ...
-│-- labels/
-│   ├── 000000000.txt
-│   ├── 000000001.txt
-│   ├── ...
+
+```plaintext
+Multiclass Object Detection Dataset/
+|-- images/
+|   |-- 000000000.png
+|   |-- 000000001.png
+|   |-- ...
+|-- labels/
+|   |-- 000000000.txt
+|   |-- 000000001.txt
+|   |-- ...
+```
 
 Components
 Images: RGB images of the soup can in .png format.