Image-Classification

This Colab notebook showcases an image classification model trained to classify images into different categories. The model has been trained on a dataset comprising images from various classes, including anacephaly, congi, down_syndrome, encephalocele, and placenta.

This project aims to showcase the process of building an image classification model and provide a starting point for further exploration and experimentation. if you want what i do , juse data, found in dataset folder extract subfolder

Dataset

The dataset used for training, validation, and testing the model is loaded using the tf.keras.utils.image_dataset_from_directory function. It is organized in a directory structure where each class has its own subdirectory containing the corresponding images. The dataset is divided into training, validation, and test sets using a 70-20-10 split.

Installation

1. Clone the repository:

   git clone https://github.com/abelyo252/Image-Classification.git
   cd image-classification

2. Install the required dependencies:

   pip install -r requirements.txt

3. Usage

   Prepare your dataset: Organize your images into separate directories for each class. use this notebook to train based on your custom data, but make sure change hyper-parameter of this model.

4. Inference:

       import tensorflow as tf
       import numpy as np
       import cv2
   
       # Load the trained model
       model = tf.keras.models.load_model('load_your_model.h5')
   
       # Load and preprocess the input image
       image = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))
       image = tf.keras.preprocessing.image.img_to_array(image)
   
       # Preprocess the image
       image = cv2.resize(image, (256, 256))
       img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
       # Preprocess the image
       input_image = np.expand_dims(img_rgb, axis=0)  # Add a batch dimension
       input_image = input_image / 255.0  # Normalize the image
       
       # Make predictions
       predictions = model.predict(input_image)
       # Get the predicted class labels
       predicted_labels = tf.argmax(predictions, axis=1)
       
       # Map the predicted labels to class names
       predicted_class_names = [class_names[label] for label in predicted_labels]
       # Print the predicted class names
       print("Predicted Class Names:", predicted_class_names)
   
   
       Replace 'path/to/image.jpg' with the path to your input image.

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Support

You can ask questions and join the development discussion:

• @ Telegram t.me/@benyohanan

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Opening an issue

You can also post bug reports and feature requests (only) in GitHub issues.

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Opening a PR

We welcome contributions! Before opening a PR, please read our contributor guide.