Datasets:
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_bytes: 65880607.6
num_examples: 16
- name: test
num_bytes: 15634112.4
num_examples: 4
download_size: 81521051
dataset_size: 81514720
configs:
- config_name: default
data_files:
- split: train
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. This HuggingFace dataset is a 20 image and label sample, but you can get the rest at no cost by creating a FalconCloud account. 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. 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.