Welcome to the "Image Classification with CNNs" project repository! This project is a deep dive into the world of computer vision and deep learning. Our goal is to create a powerful image classification model using Convolutional Neural Networks (CNNs) that can recognize and categorize objects within images with impressive accuracy.
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CNN Architecture: Dive into the heart of computer vision with a state-of-the-art CNN architecture designed to learn and extract intricate features from images.
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Dataset: Utilize the popular CIFAR-10 dataset, consisting of 60,000 32x32 color images across ten distinct categories. The dataset is perfectly suited for training our image classifier.
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Training: Train the CNN model from scratch, allowing it to learn patterns and features that distinguish objects in images.
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Evaluation: Assess the performance of your model with essential metrics such as accuracy and loss. Visualize the training process to observe improvements.
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Custom Image Classification: Test your model on custom images to see its practical application beyond the training data.
- Image classification is a foundational task in computer vision with extensive real-world applications.
- Gain hands-on experience with deep learning and CNNs, making this repository an ideal starting point for those new to these technologies.
- Clone the repository to your local machine.
- Follow the step-by-step guide in the provided Jupyter notebook to build and train your image classification model.
- Explore the results and visualize the training process.
- Test your model on custom images to see it in action.
Contributions and improvements to this repository are welcome! Feel free to submit pull requests, open issues, or provide feedback to enhance this project.