Update README.md

README.md

@@ -7,13 +7,13 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 63835418.6
+    num_bytes: 63282156.6
     num_examples: 16
   - name: test
-    num_bytes: 15630561.4
+    num_bytes: 16183823.4
     num_examples: 4
-  download_size: 79472122
-  dataset_size: 79465980.0
+  download_size: 79471863
+  dataset_size: 79465980
 configs:
 - config_name: default
   data_files:
@@ -21,4 +21,98 @@ configs:
     path: data/train-*
   - split: test
     path: data/test-*
+license: apache-2.0
+task_categories:
+- object-detection
+- multiple-choice
+language:
+- en
+tags:
+- object
+- objectDetection
+- detection
+- cpg
+- indoor
+- label
+- labels
+- labeled
+- multiInstance
+size_categories:
+- 1K<n<10K
 ---
+
+# Multi Instance 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!
+
+ Just [create an EDU account here](https://falcon.duality.ai/secure/documentation/ex4-dataset?sidebarMode=learn&utm_source=huggingface&utm_medium=dataset&utm_campaign=multiinstance). 
+
+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/ex4-dataset?sidebarMode=learn&utm_source=huggingface&utm_medium=dataset&utm_campaign=multiinstance). Once you verify your email, the link will redirect you to the dataset page.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c8dc99951843ca6762fe02/GqElohYF9EadfofBvG6vs.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?
+- Multi Instance 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 a digital twin of your choosing! [Create an account and follow this tutorial to learn how](https://falcon.duality.ai/secure/documentation/ex2-objdetection-newtwin?sidebarMode=learn&utm_source=huggingface&utm_medium=dataset&utm_campaign=multiinstance).
+
+# Dataset Structure
+
+The dataset is organized as follows:
+
+```plaintext
+Multi Instance Object Detection Dataset/
+|-- images/
+|   |-- 000000000.png
+|   |-- 000000001.png
+|   |-- ...
+|-- labels/
+|   |-- 000000000.txt
+|   |-- 000000001.txt
+|   |-- ...
+```
+
+### Components
+
+Images: RGB images of the object in `.png` format.
+
+Labels: Text files (`.txt`) containing bounding box annotations for each class:
+- 0 = soup
+
+### Example Annotation (YOLO Format):
+
+```plaintext
+0 0.475 0.554 0.050 0.050
+0 0.685 0.264 0.070 0.128
+```
+
+- 0 represents the object class (soup can).
+- The next four values represent the bounding box coordinates (normalized x_center, y_center, width, height).
+- Multiple lines are annotations for multiple instances
+
+### 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-Multi-Instance-Object-Detection-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
+Attribution: If you use this dataset in research or commercial projects, please provide appropriate credit.