Cat vs Dog Image Classification using CNN
Nathanael Mbale
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
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
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 percentages age
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
Technologies Used
TensorFlow 2.0
Keras
Convolutional Neural Networks
Computer Vision
Data Augmentation
Model Evaluation and Visualization
Key Skills Demonstrated
Problem solving through overfitting detection and mitigation
End-to-end machine learning pipeline development
Clean, structured experimentation and documentation
Results-driven modeling meeting defined accuracy benchmarks
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Posted Dec 27, 2025
Built a CNN to classify cat and dog images with 72% accuracy.
