Smoke and Fire Classification using Transfer Learning

This project focuses on classifying images into two categories: Smoke and Fire, using deep learning and transfer learning techniques. We use EfficientNetB0 as a pre-trained model, perform fine-tuning, and visualize performance through evaluation metrics and graphs.

🧠 Project Highlights

• Problem: Binary classification of images into "Smoke" or "Fire"
• Dataset: Custom dataset organized into train/test folders
• Preprocessing:
  • Image resizing to 224x224
  • Normalization
  • Label encoding
• Visualization:
  • Class distribution
  • Sample images

📦 Techniques Used

• Transfer Learning with EfficientNetB0
• Feature Extraction and Fine Tuning
• Partial Layer Freezing
• Data Augmentation (optional)
• Model Evaluation: Accuracy, Precision, Recall, F1-Score, Confusion Matrix
• Deployment Phase: Prediction on unseen images

---

🛠 Libraries and Frameworks

• TensorFlow / Keras
• NumPy
• Matplotlib
• Seaborn
• scikit-learn
• OpenCV

---

📊 Key Features

• Train a deep learning model using EfficientNetB0.

• Fine-tune pre-trained models on a custom Smoke/Fire dataset.

• Evaluate performance using:

• Accuracy

• Precision

• Recall

• Confusion Matrix

• Classification Report

---

📈 Model Performance

Metric Value

Test Accuracy ~76.0%

Precision (Fire) 1.0000

Recall (Fire) 0.5200

Precision (Smoke) 0.6757

Recall (Smoke) 1.0000

---

📸 Sample Outputs

• Training and Validation Accuracy/Loss Curves

• Confusion Matrix Visualization

• Classification Report

• Predictions on Unseen Images