Duplicate from naman-jain7/convnext-solar-panel-defect-classifier-v1

Co-authored-by: Naman Jain <naman-jain7@users.noreply.huggingface.co>

.gitattributes

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+*.pb filter=lfs diff=lfs merge=lfs -text
+variables/* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: en
+license: mit
+tags:
+- solar-panel
+- defect-detection
+- convnext
+- computer-vision
+- tensorflow
+- sustainability
+- renewable-energy
+datasets:
+- custom-solar-panel-defect
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+library_name: tensorflow
+---
+
+# ☀️ ConvNeXt Solar Panel Defect Classifier 🔍
+
+[![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange?logo=tensorflow)](https://www.tensorflow.org/)
+[![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue)](https://huggingface.co/models/naman-jain7/convnext-solar-panel-defect-classifier-v1)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/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.
+
+![Training Metrics](assets/training.png)
+
+### 🧪 Error Analysis
+A deeper look into model predictions via the Confusion Matrix and ROC Curve.
+
+<p align="center">
+  <img src="assets/conf_matrix.png" width="45%" title="Confusion Matrix" />
+  <img src="assets/roc.png" width="45%" title="ROC Curve" />
+</p>
+
+### 📋 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}
+}
+```

fingerprint.pb

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labels.json

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+{
+    "0": "snow-covered",
+    "1": "physical-damage",
+    "2": "electrical-damage",
+    "3": "dusty",
+    "4": "clean",
+    "5": "bird-drop"
+}

requirements.txt

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+tensorflow==2.19.0
+numpy
+pillow
+gradio

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