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---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_bytes: 63282156.6
num_examples: 16
- name: test
num_bytes: 16183823.4
num_examples: 4
download_size: 79471863
dataset_size: 79465980
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: apache-2.0
task_categories:
- object-detection
- multiple-choice
language:
- en
tags:
- object
- objectDetection
- detection
- cpg
- indoor
- label
- labels
- labeled
- multiInstance
size_categories:
- 1K<n<10K
---
# Multi Instance 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/ex4-dataset?sidebarMode=learn&utm_source=huggingface&utm_medium=dataset&utm_campaign=multiinstance).
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/ex4-dataset?sidebarMode=learn&utm_source=huggingface&utm_medium=dataset&utm_campaign=multiinstance). 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?
- Multi Instance 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 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=multiinstance).
# Dataset Structure
The dataset is organized as follows:
```plaintext
Multi Instance Object Detection Dataset/
|-- 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):
```plaintext
0 0.475 0.554 0.050 0.050
0 0.685 0.264 0.070 0.128
```
- 0 represents the object class (soup can).
- The next four values represent the bounding box coordinates (normalized x_center, y_center, width, height).
- Multiple lines are annotations for multiple instances
### Usage
This dataset is designed to be used with popular deep learning frameworks. Run these commands:
```plaintext
from datasets import load_dataset
```
```plaintext
dataset = load_dataset("your-huggingface-username/YOLOv8-Multi-Instance-Object-Detection-Dataset")
```
To train a YOLOv8 model, you can use Ultralytics' yolo package:
```plaintext
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. |