image imagewidth (px) 1.92k 1.92k	label stringlengths 53 79
	0 0.5015625 0.46574074074074073 0.09270833333333334 0.18703703703703703
	0 0.62578125 0.1837962962962963 0.16510416666666666 0.3675925925925926
	0 0.49765625 0.5365740740740741 0.13072916666666667 0.28055555555555556
	0 0.6716145833333333 0.6601851851851852 0.2109375 0.3
	0 0.4080729166666667 0.19814814814814816 0.20052083333333334 0.3962962962962963
	0 0.4583333333333333 0.5125 0.240625 0.3990740740740741
	0 0.48984375 0.5856481481481481 0.2796875 0.600925925925926
	0 0.5502604166666667 0.5259259259259259 0.1703125 0.2037037037037037
	0 0.4822916666666667 0.44305555555555554 0.08125 0.22685185185185186
	0 0.525 0.5222222222222223 0.08541666666666667 0.2388888888888889
	0 0.68203125 0.6106481481481482 0.21510416666666668 0.36203703703703705
	0 0.45338541666666665 0.7944444444444444 0.1484375 0.2462962962962963
	0 0.5333333333333333 0.6481481481481481 0.15104166666666666 0.3351851851851852
	0 0.5208333333333334 0.33796296296296297 0.0875 0.1574074074074074
	0 0.425 0.3976851851851852 0.1375 0.15092592592592594
	0 0.47682291666666665 0.36666666666666664 0.0921875 0.26296296296296295

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!

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.