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duality-robotics/YOLOv8-Multiclass-Object-Detection-Dataset

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… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/YOLOv8-Multiclass-Object-Detection-Dataset.

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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Dataset Card

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:

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

Components

  1. 1.Images: RGB images of the object in .png format.
  2. 2.Labels: Text files (.txt) containing bounding box annotations for each class
  3. 3.0 = cheerios
  4. 4.1 = soup

Licensing

license: apache-2.0