AdilShamim8 / Cat_Vs_Dog_Image_Classification_Project

A deep learning CNN project that teaches a model to distinguish cats from dogs.

Cat vs. Dog Image Classification

Welcome to the Cat vs. Dog Image Classification Project. This isn't just another machine learning exercise; it's a testament to how beautifully simple ideas can transform complex problems. Imagine empowering computers to see, think, and decide – effortlessly distinguishing between your cuddly cat and loyal dog.

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Why This Project Matters

In a world overflowing with data, meaningful patterns lie hidden in pixels. By teaching machines to differentiate between cats and dogs, we unlock the power to:

• Enhance Real-World Applications From wildlife monitoring to smart homes, image classification branches into countless domains.
• Elevate Computer Vision Mastering fundamental classification paves the way for advanced AI systems.
• Democratize AI Education A clear, engaging walkthrough that invites beginners and experts to learn, build, and innovate.

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Core Features

1. Elegant Model Architecture A streamlined Convolutional Neural Network (CNN) design balancing performance with simplicity.
2. Intuitive Preprocessing Pipeline Automated data augmentation and resizing for robust, real-time-ready datasets.
3. Hands-On Notebooks Step-by-step Jupyter notebooks guiding you from raw images to a deployed model.
4. Clear Evaluation Metrics Precision, recall, F1-score – metrics that speak directly to real-world impact.

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🛠 Technologies

• Python: Language of choice for readable, maintainable code.
• TensorFlow & Keras: Powering the deep learning engine.
• NumPy & Pandas: Data manipulation at lightning speed.
• Matplotlib & Seaborn: Crafting insightful visualizations.
• Jupyter Notebooks: Interactive, narrative-driven development.

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Installation

1. Clone the Repository

   git clone https://github.com/AdilShamim8/Cat_Vs_Dog_Image_Classification_Project.git
   cd Cat_Vs_Dog_Image_Classification_Project

2. Create Virtual Environment

   python3 -m venv venv
   source venv/bin/activate  # macOS/Linux
   venv\Scripts\activate   # Windows

3. Install Dependencies

   pip install -r requirements.txt

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Usage

1. Prepare Your Dataset Organize images into data/train/cats, data/train/dogs, data/validation/cats, and data/validation/dogs.

2. Train the Model

   python train.py --epochs 20 --batch_size 32

3. Evaluate Performance

   python evaluate.py --model_path models/cat_dog_classifier.h5

4. Predict New Images

   python predict.py --image_path path/to/image.jpg

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Results

After just 20 epochs, our model achieves:

• Accuracy: 92%
• F1-Score: 0.91
• Inference Time: 15 ms per image

Witness how precision meets elegance.

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Contributing

Your creativity fuels this project. Feel free to:

• Open issues & suggest enhancements.
• Submit pull requests with new architectures or optimizations.
• Share your success stories.

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License

This project is released under the MIT License.

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Crafted with passion, built for visionaries. Let’s change the way machines see the world – one pixel at a time.