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Swarmdrones/
convnext-solar-panel-defect-classifier-v1

Name: convnext-solar-panel-defect-classifier-v1
Availability: InStock

custom-solar-panel-defect

mit

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☀️ ConvNeXt Solar Panel Defect Classifier 🔍

TensorFlow

License: MIT

📖 Overview

This model is designed to automate the inspection of solar panels within a microgrid ecosystem. By identifying defects such as physical damage, dust accumulation, or electrical failures, it helps in maintaining peak efficiency and reducing manual maintenance costs.

The classifier is built upon the ConvNeXt-Tiny architecture, a modern pure convolutional neural network that rivals Transformers in performance while maintaining the efficiency of standard CNNs.

🎯 Key Features

Modern Architecture: Leverages ConvNeXt-Tiny for high-accuracy feature extraction.
Robust Performance: Achieved 97.18% Test Accuracy.
6-Class Classification: Specialized in detecting specific solar panel states.

🛠️ Model Details

Base Model: ConvNeXt-Tiny (Pretrained on ImageNet-1K)
Input Resolution: 224x224 px
Optimization: Adam Optimizer with Categorical Cross-Entropy loss.
Framework: TensorFlow / Keras

📊 Dataset & Classes

The model was fine-tuned on a curated dataset of solar panel imagery, specifically labeled for microgrid maintenance scenarios.

Label	Description
❄️ Snow-covered	Panels obstructed by snow
🔨 Physical-damage	Cracks, broken glass, or structural issues
⚡ Electrical-damage	Burning marks or internal circuit failure
🌫️ Dusty	High accumulation of dirt/sand
✨ Clean	Fully operational and clear panels
🐦 Bird-drop	Obstruction due to wildlife

📈 Performance & Metrics

🔄 Training Progress

Monitoring the learning curves for both accuracy and loss.

🧪 Error Analysis

A deeper look into model predictions via the Confusion Matrix and ROC Curve.

📋 Classification Report

Class	Precision	Recall	F1-Score	Support
Bird-drop	0.95	0.90	0.93	41
Clean	0.86	1.00	0.92	30
Dusty	0.94	0.92	0.93	36
Electrical-damage	1.00	0.97	0.99	39
Physical-damage	1.00	0.97	0.98	32
Snow-covered	1.00	1.00	1.00	46
Average / Total	0.96	0.96	0.96	224

🚀 Usage

python

from inference import predict

# Load image and predict
image_path = "solar_panel_test.jpg"
result = predict(image_path)

print(f"Prediction: {result['class']}")
print(f"Confidence: {result['confidence']:.2%}")

📄 License

This project is licensed under the MIT License.

🤝 Citation

If you use this model in your research or project, please cite:

bibtex

@model{convnext_solar_defect_2024,
  author = {Microgrid Efficiency Project Team},
  title = {ConvNeXt Solar Panel Defect Classifier},
  year = {2024},
  publisher = {Hugging Face Hub}
}

Swarmdrones/convnext-solar-panel-defect-classifier-v1