This project uses a Convolutional Neural Network (CNN) to classify images of different objects. The goal is to train the neural network to recognize and distinguish between objects in an image, such as cars, animals, and other everyday objects.
The data used for this project is a collection of images of different objects. The dataset can be obtained from various sources, such as the ImageNet database, CIFAR-10 or CIFAR-100 dataset, or custom datasets created for specific projects.
The CNN model is built using Keras with Tensorflow as the backend. The training process involves several steps, including data preprocessing, model building, and optimization. The model is trained using a set of labeled images, and the training accuracy is monitored to determine when to stop training.
Once the model is trained, it is evaluated using a separate set of test images. The accuracy of the model is calculated by comparing the predicted labels with the actual labels of the test images. The evaluation results can be used to determine the effectiveness of the model and to identify areas for improvement.
To use the model, simply run the predict.py script and provide an image file as input. The script will return the predicted class label for the input image.
Object Classification CNN is a powerful technique for image recognition tasks, and this project demonstrates how to train a neural network to classify images of different objects. With further optimization and tuning, the model can be improved to achieve even higher accuracy.
This project is licensed under the MIT License. See the LICENSE file for more details.