Clean/Littered Road Classification
Clean/Littered Road Classification
Datasets
Clean/Littered Road Classification
File
Clean/Littered Road Classification
Use Case
Road Classification
Description
The main goal of the clean/littered road classification task is to develop a system that can automatically classify images or video frames of roads into two categories: "clean" and "littered".
About Dataset
Our “Road Cleanliness Classification” dataset contains a total of 237 images sourced from various online platforms. The aim of this model is to determine whether a road is clean or littered with junk and trash.
Challenges and Opportunities
One challenge of this dataset is its small size. To overcome this, coders can use pre-existing models and enhance their data with augmentation techniques. Data augmentation and pre-trained models can expand the dataset’s capabilities and improve the model’s accuracy.
We encourage you to explore and utilize this valuable tool to advance smart solutions for classifying road cleanliness. Whether you’re an AI enthusiast, academic, or industry professional, this dataset is a valuable resource for any AI project.
Unlock the potential of our “Road Cleanliness Classification” dataset and empower your AI projects today!
For the dataset download, kindly visit our dedicated page: Clean/Littered Road Classification Dataset
Conclusion
Globose Technology Solutions Private Limited leads in Clear/sloppy avenue allocation, using advanced technologies to accurately categorize urban avenue. Our commitment to creating cleaner and more sustainable cities is reflected in our innovative solutions, which leverage machine learning and image analysis. Globose aims to provide municipalities with valuable insights for effective waste management, contributing to the vision of smarter and cleaner urban environments.
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