README.md

---
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](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/ex2-dataset?sidebarMode=learn&highlight=dataset&utm_source=huggingface&utm_medium=dataset&utm_campaign=soupCan). 

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.

# 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:

```plaintext
Multiclass 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.