Shahupdates / cnnClassification

using a convolutional neural network using TensorFlow for image classification

cnnClassification

This code builds a convolutional neural network (CNN) using the TensorFlow library. The CNN has three convolutional layers followed by max pooling layers, and a fully connected layer with 256 neurons. It uses the softmax activation function to output a probability distribution over 10 classes. The model is compiled with the Adam optimizer and the categorical cross-entropy loss function.

The code then creates data generators using the ImageDataGenerator class to perform data augmentation, rescaling the pixel values of the images to be between 0 and 1. It trains the model using the fit method on the training data generator, and evaluates the model on the test data generator using the evaluate method.

This code can be used to classify images into one of ten categories, such as different types of animals or objects. The performance of the model can be improved by tuning hyperparameters such as the number of layers, the number of neurons per layer, and the learning rate of the optimizer.

Convolutional Neural Network (CNN) Image Classification

This project demonstrates how to build a Convolutional Neural Network (CNN) model for image classification using TensorFlow and Keras.

Table of Contents

• Overview
• Installation
• Usage
• Dataset
• Model Architecture
• Training
• Evaluation
• Results
• Contributing
• License

Overview

This project implements a CNN model to classify images into multiple categories. The code is written in Python and uses the TensorFlow and Keras libraries for deep learning.

Installation

1. Clone the repository:

git clone https://github.com/shahupdates/cnnClassification

2. Install the required dependencies:

pip install tensorflow keras

Usage

1. Prepare your dataset by organizing images into separate directories for each class.

2. Adjust the hyperparameters and model architecture in the image_classifier.py file according to your requirements.

3. Train the model: No-Gui: python image_classifier.py Gui: python gui.py

4. Evaluate the model: python image_classifier.py --mode eval

Dataset

The dataset used for training and testing the model should be organized into separate directories, with each directory representing a class. The train and test directories should contain the training and testing images, respectively.

Model Architecture

The CNN model architecture consists of multiple convolutional layers with ReLU activation, followed by max pooling layers. The output is then flattened and passed through fully connected layers to produce the final classification probabilities using the softmax activation.

Training

During training, the images are preprocessed and augmented using data generators provided by Keras. The model is compiled with the Adam optimizer and categorical cross-entropy loss. The training progress is logged and saved to a history object.

Evaluation

After training, the model is evaluated on the test dataset to measure its performance. The test loss and accuracy are reported.

Results

The results of the evaluation, including the test loss and accuracy, are displayed in the console.

Contributing

Contributions are welcome! If you have any ideas, suggestions, or improvements, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.