Caltech-101 Image Classification

This project applies Convolutional Neural Networks (CNNs) with transfer learning (EfficientNet-B6) to classify images from the Caltech-101 dataset. The dataset contains 101 object categories (animals, vehicles, instruments, etc.), making it a challenging image classification task.

✅ Training Accuracy: 98.56% ✅ Validation Accuracy: 93.00%

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📂 Contents

• Caltech101_EfficientNet.ipynb – Main notebook with model implementation and training
• requirements.txt – Dependencies used in the project
• training_log.csv – Training curves (accuracy/loss per epoch)

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⚙️ Requirements

• Python 3.x
• TensorFlow / Keras
• NumPy
• Matplotlib
• scikit-learn

Install dependencies:

pip install -r requirements.txt

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🚀 How to Run

1. Download the Caltech-101 dataset from Caltech Dataset Page or via TensorFlow Datasets.
2. Place the dataset in a folder named data/ (or update the dataset path in the notebook).
3. Run the notebook:

jupyter notebook Caltech101_EfficientNet.ipynb

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🏷️ Topics

cnn image-classification deep-learning transfer-learning efficientnet caltech101

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