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docs/en/datasets/segment/crack-seg.md
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@ -6,9 +6,9 @@ keywords: Roboflow, Crack Segmentation Dataset, Ultralytics, transportation safe
# Roboflow Universe Crack Segmentation Dataset
The [Roboflow](https://roboflow.com/?ref=ultralytics) [Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) stands out as an extensive resource designed specifically for individuals involved in transportation and public safety studies. It is equally beneficial for those working on the development of self-driving car models or simply exploring computer vision applications for recreational purposes.
The [Roboflow](https://roboflow.com/?ref=ultralytics) [Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) stands out as an extensive resource designed specifically for individuals involved in transportation and public safety studies. It is equally beneficial for those working on the development of self-driving car models or simply exploring [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications for recreational purposes.
Comprising a total of 4029 static images captured from diverse road and wall scenarios, this dataset emerges as a valuable asset for tasks related to crack segmentation. Whether you are delving into the intricacies of transportation research or seeking to enhance the accuracy of your self-driving car models, this dataset provides a rich and varied collection of images to support your endeavors.
Comprising a total of 4029 static images captured from diverse road and wall scenarios, this dataset emerges as a valuable asset for tasks related to crack segmentation. Whether you are delving into the intricacies of transportation research or seeking to enhance the [accuracy](https://www.ultralytics.com/glossary/accuracy) of your self-driving car models, this dataset provides a rich and varied collection of images to support your endeavors.
## Dataset Structure
@ -34,7 +34,7 @@ A YAML (Yet Another Markup Language) file is employed to outline the configurati
## Usage
To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -129,7 +129,7 @@ The Crack Segmentation Dataset is exceptionally suited for self-driving car proj
### What unique features does Ultralytics YOLO offer for crack segmentation?
Ultralytics YOLO offers advanced real-time object detection, segmentation, and classification capabilities that make it ideal for crack segmentation tasks. Its ability to handle large datasets and complex scenarios ensures high accuracy and efficiency. For example, the model [Training](../../modes/train.md), [Predict](../../modes/predict.md), and [Export](../../modes/export.md) modes cover comprehensive functionalities from training to deployment.
Ultralytics YOLO offers advanced real-time [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification capabilities that make it ideal for crack segmentation tasks. Its ability to handle large datasets and complex scenarios ensures high accuracy and efficiency. For example, the model [Training](../../modes/train.md), [Predict](../../modes/predict.md), and [Export](../../modes/export.md) modes cover comprehensive functionalities from training to deployment.
### How do I cite the Roboflow Crack Segmentation Dataset in my research paper?