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.
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.
- Elegant Model Architecture A streamlined Convolutional Neural Network (CNN) design balancing performance with simplicity.
- Intuitive Preprocessing Pipeline Automated data augmentation and resizing for robust, real-time-ready datasets.
- Hands-On Notebooks Step-by-step Jupyter notebooks guiding you from raw images to a deployed model.
- Clear Evaluation Metrics Precision, recall, F1-score – metrics that speak directly to real-world impact.
- 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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Clone the Repository
git clone https://github.com/AdilShamim8/Cat_Vs_Dog_Image_Classification_Project.git cd Cat_Vs_Dog_Image_Classification_Project -
Create Virtual Environment
python3 -m venv venv source venv/bin/activate # macOS/Linux venv\Scripts\activate # Windows
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Install Dependencies
pip install -r requirements.txt
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Prepare Your Dataset Organize images into
data/train/cats,data/train/dogs,data/validation/cats, anddata/validation/dogs. -
Train the Model
python train.py --epochs 20 --batch_size 32
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Evaluate Performance
python evaluate.py --model_path models/cat_dog_classifier.h5
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Predict New Images
python predict.py --image_path path/to/image.jpg
After just 20 epochs, our model achieves:
- Accuracy: 92%
- F1-Score: 0.91
- Inference Time: 15 ms per image
Witness how precision meets elegance.
Your creativity fuels this project. Feel free to:
- Open issues & suggest enhancements.
- Submit pull requests with new architectures or optimizations.
- Share your success stories.
This project is released under the MIT License.
Crafted with passion, built for visionaries. Let’s change the way machines see the world – one pixel at a time.