CIFAR-10_Image_Classification_with_CNN

This project demonstrates the implementation of a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset into one of 10 categories. The workflow includes data preprocessing, augmentation, model training, and evaluation. This notebook is ideal for understanding image classification tasks using deep learning techniques.

Project_Overview

The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images. This project builds a CNN model using TensorFlow/Keras to achieve a high classification accuracy on this dataset.

Dataset_Information

• Dataset: CIFAR-10
• Classes: Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck
• Image Size: 32x32 pixels

Project_Structure

1. Loading CIFAR-10 Dataset: The dataset is loaded and split into training and testing sets.
2. Preprocessing the Data: Images are normalized to improve model performance.
3. Data Augmentation: Techniques like random flips and rotations are applied to enrich the dataset and reduce overfitting.
4. Building the CNN Model: A CNN architecture is designed with convolutional, pooling, and fully connected layers.
5. Compiling the Model: The model is compiled with an optimizer, loss function, and evaluation metric.
6. Training the Model: The model is trained on the dataset with a learning rate scheduler and early stopping.
7. Evaluating the Model: Test accuracy and loss are reported to measure performance.

Results

• Test Accuracy: Achieved approximately 90% accuracy on the test set.
• Test Loss: Demonstrated effective generalization with a reduced loss value.

Prerequisites

• Python 3.8 or later
• TensorFlow 2.x
• Jupyter Notebook or any Python IDE

Instructions_to_Run

1. Clone the repository:

   git clone https://github.com/<your-username>/CIFAR-10-CNN-Classifier.git

2. Navigate to the project directory:

   cd CIFAR-10-CNN-Classifier

3. Install required dependencies:

   pip install -r requirements.txt

4. Open the notebook:

   jupyter notebook "Image Classification with CNN.ipynb"

5. Run all cells in the notebook to reproduce results.

Model_Summary

The CNN model includes the following layers:

• Convolutional Layers: Extract features from images
• Pooling Layers: Reduce dimensionality while retaining essential features
• Fully Connected Layers: Perform classification based on extracted features
• Dropout Layers: Prevent overfitting during training

Conclusion

This project successfully classifies CIFAR-10 images with high accuracy, demonstrating the effectiveness of CNNs in image recognition tasks. The use of data augmentation and learning rate scheduling significantly contributed to the model's performance.

Future_Work

• Implement a web-based frontend for live predictions.
• Experiment with transfer learning using pretrained models.
• Extend to CIFAR-100 for more complex classification tasks.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Feel free to contribute to this project or use it as a learning resource!