Welcome to the Everyday Objects Classifier project! This repository addresses the challenge of classifying 10 different everyday objects using TensorFlow's CIFAR Image Dataset 🧭🔍. With 60,000 colorful images and 6000 images per class, we embark on a thrilling machine learning journey to achieve accurate object recognition! 🚀🔮

The CIFAR Image Dataset contains 60,000 32x32 color images, categorized into 10 classes. Each class represents an everyday object, and the dataset is evenly distributed, with 6000 images per class. Below is a table summarizing the dataset information:

1. Clone the repository: git clone https://github.com/Asirwad/CIFAR-Image-Classifier-Tensorflow.git
2. Install the required dependencies: pip install tensorflow matplotlib
3. Explore the Jupyter notebook to understand data preprocessing and model building.
4. Train the classifier and achieve top-notch object recognition! 📸🌟

The Everyday Objects Classifier uses a deep learning architecture to handle the image classification task. With advanced TensorFlow techniques, we strive to optimize the model's performance 🏆🌈.

Contributions are welcome! Whether it's enhancing the model's accuracy, improving the documentation, or adding new features, your contributions can elevate the object recognition experience for everyone! 🚀🔍 Join us in shaping the future of this exciting image classification project! 📸🌆

Special thanks to the TensorFlow team for providing the CIFAR Image Dataset and the amazing machine learning community for their continuous support!

🖼️🏆 Let's classify everyday objects with the magic of machine learning! 🖼️🏆