Cats vs Dogs Classification (SVM)
88% classification accuracy
Cats vs Dogs Classification was born from a playful challenge: can classical ML still compete? Built during my Prodigy Info Tech internship, I crafted a system where SVM meets pixels, where features tell species, and where 88% accuracy proves that sometimes, the classic approach is the winning move.
1 / 288% classification accuracy
Binary classification
SnapshotAt a glance
ML Engineer (Prodigy Info Tech Intern)
1 month
88% classification accuracy
Binary classification
Technologies Used
5 Technologies Integrated
Impact
Key Features
Feature Implementation
Project Vision
Achieve accurate binary classification using SVM.
Core Process
The process of Developing it.
Preprocessed images, extracted features, trained SVM classifier, and evaluated performance.
Build notesWhat I built
03Implemented a binary image classification system using Support Vector Machine (SVM) with Scikit-learn, achieving 88% classification accuracy by developing advanced feature extraction techniques from image pixels, proving that classical ML approaches can still compete effectively in image recognition tasks.
Built a comprehensive image preprocessing pipeline using OpenCV that handles image variations, normalizes features, and prepares data for SVM classification, creating a system where SVM meets pixels and features tell species with reliable accuracy.
Developed an optimized SVM classifier with parameter tuning and feature engineering, demonstrating that sometimes the classic approach is the winning move, achieving 88% accuracy in distinguishing between cats and dogs through traditional machine learning techniques rather than deep learning.
Exploring classical ML approaches for image classification.
Features
- Binary image classification
- Feature extraction & evaluation
- SVM model implementation
- Image preprocessing
Challenges
- Feature extraction
- Model optimization
- Handling image variations
