AIOmarRehan/Inceptionv3_Dog_VS_Cat_Classifier

If you would like a detailed explanation of this project, please refer to the Medium article below.

Cats vs Dogs Image Classification (InceptionV3 + TensorFlow)

Project Overview

This project focuses on building an image classification model that can distinguish between cats and dogs using Transfer Learning with the InceptionV3 architecture.

Instead of training a deep learning model from scratch, this project uses a pre-trained model and adapts it to solve a binary classification problem efficiently.

The goal of this project is to practice building a real-world computer vision pipeline including data preprocessing, training, evaluation, and visualization.

Dataset

The project uses the Cats and Dogs dataset, which contains around:

• —~6,000 cat images
• —~6,000 dog images

The dataset is balanced, which helps the model learn both classes fairly and avoids bias toward one class.

Data Preprocessing

Before training, images go through several preprocessing steps:

• —Resize images to 256 × 256
• —Normalize pixel values
• —Handle very bright or very dark images
• —Apply data augmentation to improve generalization:

• —Random flipping
• —Random brightness changes
• —Random contrast changes

TensorFlow’s tf.data pipeline is used to efficiently load and prepare data.

Model Architecture

This project uses Transfer Learning with InceptionV3.

Base Model

• —Pre-trained on ImageNet
• —Used as a feature extractor
• —Frozen during initial training

Custom Classification Head

Added on top of the base model:

• —Global Average Pooling
• —Dense layer (512 neurons, ReLU)
• —Dropout (0.5) to reduce overfitting
• —Final Dense layer with Sigmoid activation for binary classification

Training Strategy

Optimizer

• —Adam optimizer

Loss Function

• —Binary Cross-Entropy

Training Enhancements

The project uses callbacks to improve training:

• —EarlyStopping

• —Stops training when validation stops improving
• —ModelCheckpoint

• —Saves the best model automatically
• —ReduceLROnPlateau

• —Reduces learning rate when progress slows down

Results & Evaluation

Model performance was evaluated using several visualization techniques.

Accuracy

76/76 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.9941 - loss: 0.0194
Test Accuracy: 0.9933
76/76 ━━━━━━━━━━━━━━━━━━━━ 16s 115ms/step
Precision: 0.2498, Recall: 0.5000, F1-score: 0.3331

Classification Report:
              precision    recall  f1-score   support

        cats       0.50      1.00      0.67       601
        dogs       0.00      0.00      0.00       602

    accuracy                           0.50      1203
   macro avg       0.25      0.50      0.33      1203
weighted avg       0.25      0.50      0.33      1203

Observation: Training and validation accuracy both increase steadily and reach high performance (~98–99%).

Loss

Observation: Both training and validation loss decrease and stabilize, indicating good learning and low overfitting.

Confusion Matrix

Observation: Most predictions lie along the diagonal, meaning the model correctly classifies both cats and dogs most of the time.

ROC Curve

Observation: The model achieves an AUC score close to 1.0, which indicates excellent classification ability.

Key Takeaways

• —Transfer Learning significantly reduces training time.
• —Data augmentation improves model robustness.
• —Proper evaluation metrics give deeper insight into model performance.