Golnaz-spa / Deep-Lerning

Object dDetection: we have number of picture . We want to use deep learning method to detect the picture

Object Detection with Deep Learning using Fashion MNIST Dataset

This script demonstrates how to build and train a deep learning model for object detection using the Fashion MNIST dataset. The Fashion MNIST dataset includes images of 10 types of clothing and accessories, making it a popular benchmark for classification tasks in machine learning. This guide covers data loading, preprocessing, model definition, training, evaluation, and prediction.

Features

• Data Loading: Utilizes the fashion_mnist dataset from Keras for training and testing.
• Data Preprocessing: Normalizes the pixel values of images for better model performance.
• Model Building: Constructs a neural network using Keras' Sequential API with two Dense layers.
• Model Training: Trains the model on the training data with a validation split for monitoring performance.
• Evaluation: Assesses the model's performance on the test set.
• Prediction: Demonstrates how to use the trained model to predict the class of new images.
• Model Saving and Loading: Shows how to save the trained model to disk and load it for future predictions.

Usage

1. Load and Preprocess the Data: Scale the pixel values of both the training and testing images.
2. Define the Model: Use Keras to build a Sequential model with layers designed for classification.
3. Compile the Model: Set up the model with an optimizer, loss function, and metrics for training.
4. Train the Model: Fit the model to the training data, using a portion of it for validation.
5. Evaluate the Model: Test the model's performance on unseen data.
6. Predict New Data: Use the trained model to predict the category of new images.
7. Save/Load the Model: Save the trained model to disk and load it for future predictions.

Notes

• Adjust the number of epochs based on the training performance and computational resources.
• Customize the neural network architecture to explore different model complexities.
• Ensure the new data for prediction is preprocessed in the same way as the training data.

This script is a straightforward introduction to using neural networks for image classification, providing a foundation for more complex object detection and image processing tasks.