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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 65880607.6 |
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num_examples: 16 |
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- name: test |
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num_bytes: 15634112.4 |
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num_examples: 4 |
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download_size: 81521051 |
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dataset_size: 81514720 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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license: apache-2.0 |
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task_categories: |
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- object-detection |
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language: |
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- en |
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tags: |
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- objectdetection d |
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- detection |
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- syntheticdata |
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- yolov8 |
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- yolo |
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- labels |
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- labeled |
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- label |
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- indoor |
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- cpg |
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- can |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Soup Can Object Detection Dataset Sample |
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[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! |
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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). |
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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. |
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 |
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# Dataset Overview |
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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. |
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#### Why Use This Dataset? |
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- Single Object Detection: Specifically curated for detecting soup cans, making it ideal for fine-tuning models for retail, inventory management, or robotics applications. |
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- Varied Environments: The dataset contains images with different lighting conditions, poses, and occlusions to help solve traditional recall problems in real world object detection. |
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- Accurate Annotations: Bounding box annotations are precise and automatically labeled in YOLO format as the data is created. |
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Create your own specialized data! |
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You can create a dataset like this but with 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=soupCan). |
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# Dataset Structure |
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The dataset is organized as follows: |
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```plaintext |
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Object Detection Dataset 02/ |
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|-- images/ |
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| |-- 000000000.png |
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| |-- 000000001.png |
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| |-- ... |
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|-- labels/ |
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| |-- 000000000.txt |
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| |-- 000000001.txt |
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| |-- ... |
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``` |
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### Components |
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Images: RGB images of the object in `.png` format. |
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Labels: Text files (`.txt`) containing bounding box annotations for each class: |
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- 0 = soup |
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### Example Annotation (YOLO Format): |
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```plaintext |
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0 0.475 0.554 0.050 0.050 |
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``` |
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- 0 represents the object class (soup can). |
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- The next four values represent the bounding box coordinates (normalized x_center, y_center, width, height). |
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### Usage |
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This dataset is designed to be used with popular deep learning frameworks. Run these commands: |
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```plaintext |
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from datasets import load_dataset |
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``` |
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```plaintext |
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dataset = load_dataset("your-huggingface-username/YOLOv8-Object-Detection-02-Dataset") |
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``` |
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To train a YOLOv8 model, you can use Ultralytics' yolo package: |
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```plaintext |
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yolo train model=yolov8n.pt data=soup_can.yaml epochs=50 imgsz=640 |
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``` |
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Licensing |
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License: Apache 2.0 |
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Attribution: If you use this dataset in research or commercial projects, please provide appropriate credit. |
