metadata

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.

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 a digital twin of your choosing! Create an account and follow this tutorial to learn how.

Dataset Structure

The dataset is organized as follows:

Object Detection Dataset 02/
|-- 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):

0 0.475 0.554 0.050 0.050

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. Run these commands:

from datasets import load_dataset

dataset = load_dataset("your-huggingface-username/YOLOv8-Object-Detection-02-Dataset")

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.