https://github.com/soupu07/image_classification

The project aimed to develop a machine learning model using TensorFlow and Keras to classify images of clothing items from the Fashion MNIST dataset.
https://github.com/soupu07/image_classification

data-science data-science-projects deep-learning keras-tensorflow python python-data-analysis python-programming-language tensorflow

Last synced: 3 days ago
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The project aimed to develop a machine learning model using TensorFlow and Keras to classify images of clothing items from the Fashion MNIST dataset.

• Host: GitHub
• URL: https://github.com/soupu07/image_classification
• Owner: Soupu07
• Created: 2024-10-26T21:51:30.000Z (12 days ago)
• Default Branch: main
• Last Pushed: 2024-10-26T22:44:43.000Z (12 days ago)
• Last Synced: 2024-11-05T02:04:33.524Z (3 days ago)
• Topics: data-science, data-science-projects, deep-learning, keras-tensorflow, python, python-data-analysis, python-programming-language, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 218 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Image_Classification

**Background**

You are tasked with classifying images of clothing from the Fashion MNIST dataset using a Neural Network model built with Tensorflow and Keras. The dataset includes 60,000 training images and 10,000 test images, each of size 28x28 pixels, representing different classes of fashion items.

**Questions:**

**1.**	**Data Loading and Exploration**

  	    ●	What dataset is used in this case study, and how is it loaded into the model?

   

        ●	What are the dimensions of the training and test images, and what do the labels represent?

     

        ●	How many classes are there, and what are the names of these classes?

**2.**	**Data Preprocessing**

        ●	Why is it important to scale the pixel values between 0 and 1?

        ●	What function or method is used to perform this scaling?

**3.**	**Model Building and Compilation**

        ●	What type of model architecture is used in this solution? Briefly describe its layers.

        ●	Which activation functions are used in the model, and why?

        ●	What loss function and optimizer are chosen for this model? Explain why they are suitable for this task.

**4.**	**Model Training**

        ●	Explain the concept of an epoch in model training. How many epochs were used in this case study?

        ●	What does the batch size parameter control, and how does it affect model training?

**5.** **Evaluation**

        ●	What metrics are used to evaluate the model’s performance?

        ●	What was the final test accuracy achieved by the model, and how does it compare with training accuracy?

**6.**	**Predictions and Visualizations**

        ●	How does the model make predictions, and what method is used to identify the predicted class?

        ●	What visualization techniques are used to understand the model's predictions? Explain the color coding for correct and incorrect predictions.