Update README.md

README.md

@@ -13,7 +13,7 @@ dataset_info:
     num_bytes: 15634112.4
     num_examples: 4
   download_size: 81521051
-  dataset_size: 81514720.0
+  dataset_size: 81514720
 configs:
 - config_name: default
   data_files:
@@ -21,4 +21,82 @@ configs:
     path: data/train-*
   - split: test
     path: data/test-*
+license: apache-2.0
+task_categories:
+- object-detection
+language:
+- en
+tags:
+- objectdetection d
+- detection
+- syntheticdata
+- yolov8
+- yolo
+- labels
+- labeled
+- label
+- indoor
+- cpg
+- can
+size_categories:
+- 1K<n<10K
 ---
+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.
+
+
+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.
+
+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
+The dataset is organized as follows:
+Soup-Can-Object-Detection-Dataset/
+│-- images/
+│   ├── 000000000.png
+│   ├── 000000001.png
+│   ├── ...
+│-- labels/
+│   ├── 000000000.txt
+│   ├── 000000001.txt
+│   ├── ...
+
+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):
+
+0 0.475 0.554 0.050 0.050
+
+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:
+
+from datasets import load_dataset
+
+dataset = load_dataset("your-huggingface-username/Soup-Can-Object-Detection")
+
+To train a YOLOv8 model, you can use Ultralytics' yolo package:
+
+yolo train model=yolov8n.pt data=soup_can.yaml epochs=50 imgsz=640
+
+
+Licensing
+License: Apache 2.0
+Attribution: If you use this dataset in research or commercial projects, please provide appropriate credit.