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Datasets:
duality-robotics
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YOLOv8-Multiclass-Object-Detection-Dataset

Tasks:
Object Detection
Modalities:
Image
Text
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
objectDetection
ComputerVision
vision
Synthetic
syntheticData
Yolo
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files Community
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metadata
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: string
  splits:
    - name: train
      num_bytes: 14474596.43478261
      num_examples: 20
  download_size: 18278418
  dataset_size: 18275448
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: apache-2.0
language:
  - en
task_categories:
  - object-detection
tags:
  - objectDetection
  - ComputerVision
  - vision
  - synthetic
  - syntheticData
  - Yolo
  - YOLOv8
  - multiclass
  - multiclassobjectdetection
  - training
  - free
size_categories:
  - 1K<n<10K

DATASET SAMPLE

Duality.ai just released a 1000 image dataset used to train a YOLOv8 model in multiclass 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.

What makes this dataset unique, useful, and capable of bridging the Sim2Real gap?

  • The digital twins are not generated by AI, but instead crafted by 3D artists to be INDISTINGUISHABLE to the model from the physical-world objects. This allows the training from this data to transfer into real-world applicability
  • The simulation software, called FalconEditor, can easily create thousands of images with varying lighting, posing, occlusions, backgrounds, camera positions, and more. This enables robust model training.
  • The labels are created along with the data. This not only saves large amounts of time, but also ensures the labels are incredibly accurate and reliable.

image/png

Dataset Structure

The dataset has the following structure:

Multiclass Object Detection Dataset/
|-- images/
|   |-- 000000000.png
|   |-- 000000001.png
|   |-- ...
|-- labels/
|   |-- 000000000.txt
|   |-- 000000001.txt
|   |-- ...

Components

  1. Images: RGB images of the object in .png format.
  2. Labels: Text files (.txt) containing bounding box annotations for each class
    • 0 = cheerios
    • 1 = soup

Licensing

license: apache-2.0