Summary:
In the realm of machine learning, image classification is a robust application that is becoming increasingly important in numerous industries. This article provides a comprehensive walkthrough of using AWS SageMaker, a powerful cloud-based machine learning service, to conduct an image classification task on a dataset of mobile phone images with varying surface defects.
The article begins with an overview of the dataset obtained from Kaggle, containing images of mobile phones with three types of defects – oil, scratch, and stain. The dataset also includes images of defect-free phones, and all these categories serve as the labels for the classification task.
The process starts by preparing and partitioning the dataset into training and validation sets, using Python's shutil and sklearn libraries. The script also makes use of AWS's Boto3 SDK to interact with the AWS S3 service, enabling the upload of the prepared dataset to an S3 bucket.
The AWS SageMaker's TensorFlow estimator is used to specify the machine learning model's details, including the type of instance, the framework version, the Python version, and the location of the output. Hyperparameters such as the number of epochs, batch size, and learning rate are also defined at this stage.
The TensorFlow script, written in Python, contains the model architecture which is a Convolutional Neural Network (CNN). The model uses the Keras API within TensorFlow, and the architecture involves alternating Conv2D and MaxPooling2D layers, followed by a Flatten layer, Dense layers, and a final output layer with softmax activation function to classify the images into one of the four categories.
The fit method of the TensorFlow estimator then initiates the training of the model, with the S3 URIs of the training and validation data passed as arguments. The training happens on the AWS SageMaker platform, with real-time logs displayed in the Jupyter notebook.
In the end, the article guides on how to fetch the model training history stored as a JSON file in the S3 bucket. The training and validation accuracies over epochs are then visualized using Matplotlib, a powerful Python library for data visualization.
This article serves as a detailed guide to using AWS SageMaker for image classification, from data preparation to model training, and finally, to visualization of training results. The code provided is well-commented and modular, making it easy to adapt for similar machine learning tasks.
You can access the article here.