🐱🐶 Cats vs Dogs Image Classifier using SVM

📌 Project Overview

This project implements a Support Vector Machine (SVM) model to classify images of cats and dogs. The model is trained on the Kaggle Dogs vs Cats dataset and deployed using Flask with a simple web interface.

Users can upload an image through the web application and receive a prediction indicating whether the image contains a cat or a dog.

---

🚀 Features

• Image classification using Support Vector Machine (SVM)
• Image preprocessing with OpenCV
• Image resizing and normalization
• Flask web application
• Upload image and get instant prediction
• Uploaded image preview
• Image history storage
• Clean and responsive user interface

---

🛠️ Technologies Used

• Python
• OpenCV
• NumPy
• Scikit-learn
• Flask
• HTML
• CSS

---

📂 Project Structure

task 3 C&D
│
├── history/
│   └── Uploaded Images
│
├── static/
│   └── style.css
│
├── templates/
│   └── index.html
│
├── cat_dog_svm.pkl
├── flask_app.py
├── train_model.py
└── README.md

---

📊 Dataset

Dataset used:

Kaggle Dogs vs Cats Dataset

https://www.kaggle.com/c/dogs-vs-cats

---

⚙️ Preprocessing Steps

1. Load images using OpenCV

2. Resize images to 64 × 64 pixels

3. Flatten image into a feature vector

4. Normalize pixel values to range [0,1]

5. Assign labels:

   • Cat → 0
   • Dog → 1

---

🤖 Model Training

• Algorithm: Support Vector Machine (SVM)
• Kernel: RBF
• Train-Test Split: 80% / 20%

Training Results

• Dataset Size: 5000 Images
• Accuracy: 61.10%

---

▶️ How to Run

Install Dependencies

pip install flask numpy opencv-python scikit-learn joblib

Start Flask Application

python flask_app.py

Open:

http://127.0.0.1:5000

---

📸 Web Application

1. Upload an image.
2. Click Predict.
3. View prediction result.
4. Uploaded image is displayed on the page.
5. Images are saved in the history folder.

---

🎯 Future Improvements

• CNN-based image classification
• Higher accuracy using deep learning
• Drag-and-drop image upload
• Prediction confidence score
• Image gallery for upload history
• Deploy on Render or Railway

---

👨‍💻 Author

Shresh Rahangdale

Machine Learning & Python Enthusiast