Dog vs Cat Image Classification

This project addresses a binary image classification problem: distinguishing between images of dogs and cats using deep learning. A pretrained MobileNetV2 model was employed and fine-tuned to improve performance on the target dataset.

📌 Project Overview

• Task: Classify images as either a dog or a cat.
• Model: Transfer learning with MobileNetV2 pretrained on ImageNet.
• Dataset: A labeled dataset of cat and dog images.
• Approach:
  • Used MobileNetV2 as a feature extractor initially.
  • Fine-tuned selected layers of the model for improved accuracy.
  • Applied image augmentation and normalization.
• Outcome: Achieved high classification accuracy through fine-tuning.

🚀 Technologies Used

• Python 3.x
• TensorFlow / Keras
• NumPy, Matplotlib
• Scikit-learn (for evaluation)

🧠 Model Details

• Base Model: MobileNetV2 (include_top=False)
• Fine-Tuning:
  • Unfroze top layers of MobileNetV2
  • Re-trained using a low learning rate
• Custom Head:
  • Global Average Pooling
  • Dense layer with dropout
  • Output layer with sigmoid activation