Create README.md

Accumulation of debris such as dust, snow, and bird droppings on the surface of solar panels diminishes the efficiency of the solar modules, resulting in a decrease in the amount of energy produced. Effective monitoring and cleaning of solar panels is essential for maximising module efficiency, minimising maintenance expenses, and conserving resources. Therefore, it is imperative to develop an optimal procedure for this task.@@
The purpose of this dataset is to examine the efficacy of various machine learning classifiers in accurately identifying dust, snow, bird droppings, and physical and electrical anomalies on the surfaces of solar panels.
Regarding this directory@@
This directory comprises six distinct class folders for classification. Due to the utilisation of web scraping techniques, there exists a minor disparity in the quantity of images gathered.@@
<ol>
<li><b>Pristine:</b> This directory contains images of immaculate solar panels</li>
<li><b>Dusty:</b> This directory contains photographs of solar panels covered in dust.</li>
<li><b>Bird excrement:</b> This directory contains images of bird excrement on solar panels.</li>
<li><b>Electrical damage:</b> This directory contains images of solar panels that have been affected by electrical damage.</li>
<li><b>Physical-Damage:</b> This directory contains photographs of solar panels that have incurred physical damage.</li>
<li><b>Snow-Covered:</b> This directory contains photographs of solar panels covered in snow.</li>
</ol>
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README.md

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+---
+license: apache-2.0
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- chemistry
+pretty_name: Solor Panel Defect Dataset
+size_categories:
+- 1K<n<10K
+---