README.md

---
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.
![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c8dc99951843ca6762fe02/GqElohYF9EadfofBvG6vs.png)
# 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.