🎯 Object Classification with Deep Learning

This project focuses on classifying objects using deep learning. It involves training convolutional neural networks (CNNs) on image data to recognize and distinguish between multiple object categories.

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📁 Dataset

• Source: Collected from multiple sources (including Kaggle and online resources), then manually curated and labeled.
• Classes: Boot, Chair, Laptop, Sofa, Table
• Link: Google Drive

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🧠 Techniques Used

• Image preprocessing, CNN and transfer learning (VGG16, ResNet50), model evaluation using accuracy.

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🎯 Goal

• Build a deep learning model to classify objects from images
• Experiment with transfer learning (VGG16, ResNet50) vs. custom CNN
• Evaluate classification accuracy and model performance

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✅ Results

• Best model: VGG16
• Validation accuracy: ~[95.5]%
• Model can successfully distinguish 5 object classes with high precision
• Sample predictions shown using matplotlib

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🛠️ Tools & Libraries

Python, TensorFlow, Keras
NumPy, Pandas, Matplotlib, Seaborn, VisualKeras
Google Colab, Google Drive

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⭐ This project was developed during my learning journey and reflects my ability to apply concepts into practice. It continues to be improved as I grow