Pipeline tracks Computer Vision Project

Description: The Pipeline Tracks dataset is a curated collection comprising 2000 images, each focusing on a singular class - "pipe," specifically designed for pipe detection tasks.

Dataset Overview:

• Images: 2000
• Class: Pipe

Use Case: The primary objective of this dataset is to serve as a valuable resource for training and evaluating machine learning models specialized in pipe detection within images.

Key Features:

• Diversity: The dataset encompasses a diverse range of images, capturing pipes in various environmental conditions and settings.
• Annotations: Images are annotated to facilitate model training, with precise labeling of pipe locations.

Dataset Structure:

• Images/
  • Image001.jpg
  • Image002.jpg
  • ...
• Annotations/
  • Image001.xml
  • Image002.xml
  • ...

How to Use:

1. Training: Utilize the dataset for training machine learning models, particularly those focused on detecting pipes in images.
2. Evaluation: Assess the performance of your models by testing them on the provided dataset.

Acknowledgments: The Pipeline Tracks dataset is made available by Ibrahim Aromoye, from Universiti Teknologi PETRONAS UTP, Malaysia, contributing to the advancement of object detection algorithms in the field of pipeline tracking.

Citation: If you use this dataset in your work, please cite it as follows:

Aromoye, Ibrahim Akinjobi. (2023). Pipeline Tracks Dataset. [https://universe.roboflow.com/utp-jtbn5/pipeline-tracks]

Thank you for choosing the Pipeline Tracks dataset for your pipeline detection tasks. Happy coding!