Cv Solar Panel Defect Detection
C
Cv Solar Panel Defect Detection
Developed by Bazaar
This is an image classification model built with PyTorch and HuggingPics, specifically designed for detecting defects in solar panels.
Downloads 26
Release Time : 9/5/2023
Model Overview
The model can classify solar panel images into four states: no defect, minor defect, moderate defect, and severe defect, achieving an accuracy of 80.9%.
Model Features
High Accuracy
Achieves 80.9% accuracy in defect detection tasks.
Multi-Level Classification
Can distinguish between no defect, minor defect, moderate defect, and severe defect states.
Ease of Use
Built with the HuggingPics framework for easy deployment and usage.
Model Capabilities
Image Classification
Defect Detection
Solar Panel Quality Assessment
Use Cases
Solar Industry
Production Line Quality Inspection
Automatically detects product defects on solar panel production lines.
Improves inspection efficiency and reduces labor costs.
Maintenance Inspection
Conducts regular inspections of installed solar panels.
Identifies potential issues early and extends service life.
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