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Cat vs Dog Image Classification using CNN

Content Page

  • 1.0 Problem Statement
  • 1.1 Implementation
  • 1.2 Highlights (Metrics)

1.0 Problem Statement

Build a Convolutional Neural Network capable of classifying images of cats and dogs with accuracy greater than 63 %, using a limited training dataset of 2,000 images. The key challenge is preventing overfitting while maintaining strong generalization performance.

1.1 Implementation

Quick Overview (2-Minute Read)

Problem Build a CNN to classify cat and dog images with greater than 63 % accuracy using limited training data.

Solution Implemented an end-to-end deep learning pipeline using TensorFlow and Keras, including aggressive data augmentation to combat overfitting.

Impact Delivered a functional computer vision model that reliably classifies pet images, demonstrating applied CNN knowledge and practical ML workflow execution.

Complete work

https://colab.research.google.com/drive/1prIfgPHEy1ppiIdpXizuVUBUy0RdJdKv?usp=sharing

Step-by-Step Documentation

Step 1: Environment Setup and Data Loading

  • Imported TensorFlow 2.0 and Keras
  • Loaded the Cats vs Dogs dataset

Dataset Structure

  • Training set: 2,000 images (1,000 cats, 1,000 dogs)
  • Validation set: 1,000 images (500 cats, 500 dogs)
  • Test set: 50 unlabeled images

Step 2: Data Preprocessing

  • Created ImageDataGenerator pipelines for training, validation, and test sets
  • Normalized pixel values from 0–255 to 0–1 using rescale
  • Used flow_from_directory for batch loading
  • Disabled shuffling for test data to preserve prediction order

Verification Output

Found 2000 images belonging to 2 classes
Found 1000 images belonging to 2 classes
Found 50 images belonging to 1 class

Step 3: Data Augmentation

Issue Identified High overfitting risk due to limited training data

Solution Applied Enhanced the training generator with multiple augmentation techniques:

  • Random rotations
  • Width and height shifts
  • Horizontal flips
  • Zoom variations
  • Shear transformations

Result Artificial expansion of the training dataset, improving model generalization and validation performance.

Step 4: CNN Architecture Design

Model Components

  • Multiple Conv2D layers with ReLU activation
  • MaxPooling2D layers for spatial downsampling
  • Flatten layer
  • Fully connected Dense layers
  • Final sigmoid output layer for binary classification

Compilation Settings

  • Optimizer: Adam or RMSprop
  • Loss function: Binary crossentropy
  • Metric: Accuracy

Step 5: Model Training

  • Trained using model.fit with data generators
  • Configured epochs and steps per epoch
  • Monitored validation performance during training

Outcome The model learned meaningful feature representations from augmented image data.

Step 6: Performance Analysis

  • Plotted training versus validation accuracy
  • Plotted training versus validation loss

Purpose Validated convergence behavior and monitored overfitting trends.

Step 7: Model Evaluation

  • Generated predictions on 50 unseen test images
  • Converted probability outputs into class labels
  • Visualized predictions with confidence %ages

Interpretation Each test image was labeled as cat or dog with an associated confidence score.

Step 8: Final Validation

  • Verified final accuracy exceeded the 63 % requirement
  • Project requirements successfully met

1.2 Highlights (Metrics)

Resume-Ready Metrics

Technical Results

  • Achieved 72 % classification accuracy
  • Processed 3,050 images across training, validation, and test sets
  • Applied data augmentation to reduce overfitting and improve validation accuracy
  • Generated predictions for 50 test images with confidence scores

Model Summary

Total params: 1,061,313 (4.05 MB)
Trainable params: 1,061,313 (4.05 MB)
Non-trainable params: 0 (0.00 B)

Technologies Used

  • TensorFlow 2.0
  • Keras
  • Convolutional Neural Networks
  • Computer Vision
  • Data Augmentation
  • Model Evaluation and Visualization

Key Skills Demonstrated

  1. Problem solving through overfitting detection and mitigation
  2. End-to-end machine learning pipeline development
  3. Clean, structured experimentation and documentation
  4. Results-driven modeling meeting defined accuracy benchmarks