TurtleBot Image Collection with YOLO Annotations

1,006 images of TurtleBot robots are provided with YOLO-format bounding box annotations for a single object class. The dataset, created by fhahn, is intended for training and evaluating object detection models in robotics applications.

Use Cases

• Train a YOLO-based object detection model on 1,006 annotated TurtleBot images to localize robots in visual scenes.
• Fine-tune a pre-trained detector using the single-class 'Turtlebot' bounding box annotations for robotics environment perception.
• Benchmark detection model performance on a dedicated robotics dataset containing 1,006 labeled images.

Strengths

• 1,006 images provide a defined scale for model training and validation.
• Annotations are provided in a standard YOLO format, facilitating immediate use with common detection frameworks.
• The dataset is focused on a single, clear object class (Turtlebot), reducing label complexity.

Limitations

• The dataset size of 1,006 images is relatively small for training robust deep learning models from scratch.
• Limited to a single object class (Turtlebot), restricting use to specific robotics applications.
• Potential for limited scene or environmental diversity within the 1,006 image collection.

Provenance

Source

fhahn on Hugging Face.

Collection Method

Curated collection of images with YOLO-format annotations.

Freshness

Last updated on 2025-04-01.