Building AI projects with fastai

Building AI projects with fastai

Table of Contents:

1. Introduction to Fast AI
2. Understanding Fast AI - History and Founders
3. Benefits of Fast AI for Deep Learning
4. Getting Started with Fast AI: Setting Up the Environment
5. Importing Libraries and Utility Functions
6. Gathering Data: Using Bing Image Search API
7. Data Loading and Preparation with Fast AI
8. Creating Data Loader Objects and Applying Transformations
9. Training the Model with Fast AI: Using ResNet Architecture
10. Evaluating the Model: Confusion Matrix and Top Losses
11. Data Cleaning and Augmentation with Fast AI
12. Exporting and Saving the Trained Model
13. Loading the Saved Model and Making Predictions
14. Creating an Interactive Voila Application
15. Deploying the Voila Application with Binder

Introduction to Fast AI

Fast AI is a research institute that focuses on making deep learning accessible to individuals of all levels, including beginners, intermediates, and experts in the field. Founded by Jeremy Howard and Sylvia Rachel Thomas, Fast AI aims to revolutionize the way deep learning is approached and implemented. In this article, we will explore the key features and benefits of Fast AI, as well as provide a step-by-step guide on getting started with the library.

Understanding Fast AI - History and Founders

Fast AI was founded by Jeremy Howard and Sylvia Rachel Thomas, who are renowned research scientists in the field of deep learning. Their vision was to Create a platform that simplifies the complexities of deep learning and makes it accessible to a wider audience. With their expertise and passion, they have developed Fast AI into a powerful and user-friendly library that allows individuals to build and deploy deep learning models with ease.

Benefits of Fast AI for Deep Learning

Fast AI offers numerous benefits for deep learning practitioners, regardless of their experience level. Some key advantages of Fast AI include:

1. Accessibility: Fast AI is designed to be beginner-friendly, providing a smooth learning curve for those new to deep learning.
2. Simplified Workflow: The library streamlines the entire deep learning process, from data loading to model training and deployment.
3. Pre-Trained Models: Fast AI offers an extensive collection of pre-trained models, including the widely-used ResNet architecture, which can be easily implemented for various applications.
4. Data Augmentation and Cleaning: Fast AI provides built-in tools for data augmentation and cleaning, allowing for improved model performance and accuracy.
5. Interactive Visualization: Fast AI offers interactive widgets and GUI tools, enabling users to Visualize and explore their data effectively.
6. Deployment Capabilities: With Fast AI, users can easily export and save their trained models, as well as create interactive Voila applications for deployment.

These benefits make Fast AI a powerful tool for deep learning practitioners, enabling them to achieve impressive results with minimal effort.

Getting Started with Fast AI: Setting Up the Environment

Before diving into the world of Fast AI, it is essential to set up the required environment. Firstly, ensure that Python is installed on your system. Additionally, you should have a fundamental understanding of coding in Python and high school mathematics concepts.

Next, You will need to install the Fast AI library. This can be done by using the pip Package manager and running the command: pip install fastai.

Once the library is successfully installed, you can import the necessary modules and utility functions to begin your Journey with Fast AI.

Importing Libraries and Utility Functions

To harness the power of Fast AI, it is crucial to import the required libraries and utility functions. Fast AI provides a set of notebooks, called FastBook, that contain an extensive collection of functions and resources for deep learning.

Begin by importing the FastBook package and running the setup_book function, which prepares all the necessary methods and utility functions for your program. Additionally, import the classes and functions from FastBook, such as search_image_bing and widgets, which will be used for data cleaning and manipulation.

By importing these libraries and utility functions, you are ready to move forward with building and training your deep learning models using Fast AI.

Gathering Data: Using Bing Image Search API

To develop an image classifier using Fast AI, you will need to Gather the required data. One of the most efficient ways to obtain images is by using the Bing Image Search API, which is provided by Microsoft Azure.

First, sign up for Microsoft Azure and obtain API credits worth $200. Then, access the Azure portal to retrieve your Bing Search API key. With the key in HAND, you can use the search_image_bing function from FastBook to search and retrieve images Based on specific queries.

In this tutorial, we will focus on creating a dog classifier using images of German Shepherds, black dogs, and Labradors. By passing the appropriate search queries and the Bing Search API key to the search_image_bing function, we can download a collection of images for our classifier.

Once the images are downloaded, we can visualize them and prepare them for the next steps in our model building process.

Data Loading and Preparation with Fast AI

To train a deep learning model, we need to load and prepare the downloaded data. Fast AI provides a flexible system called the "Data Block API" to handle data loading and transformation.

To convert the downloaded images into data loader objects, we need to provide four essential pieces of information: the Type of data (images), how to get a list of items (image files), how to label the items (category of dog breed), and how to create a validation set.

By leveraging the Data Block API and specifying the required information, we can create data loader objects that store the images and labels for training and validation. Additionally, we can Apply item transformations to resize the images to a standard size, ensuring uniformity in our data.

With the data loader objects in place, We Are ready to proceed with training our model using Fast AI.

Training the Model with Fast AI: Using ResNet Architecture

To train our image classifier, we will utilize the ResNet architecture, which is a powerful and widely-used convolutional neural network model in Fast AI. By leveraging transfer learning and pre-trained models, we can train our model with limited data and achieve excellent results.

Using the ResNet18 model, we can create a CNN learner object by providing the data loader object and the specified architecture. Additionally, we need to define the metric for evaluating the model's performance, such as the error rate.

By training the model for a specific number of epochs, the model will learn to classify images based on the provided labels. Throughout the training process, we can monitor the training loss, validation loss, and error rate to gauge the model's progress and adjust hyperparameters if necessary.

Evaluating the Model: Confusion Matrix and Top Losses

After training the model, it is essential to evaluate its performance to assess its accuracy and identify any potential areas for improvement. Fast AI provides tools for visualizing the model's predictions through a confusion matrix and identifying images with the highest losses.

By using the ClassificationInterpretation class and its methods, we can generate a confusion matrix that compares the actual labels with the model's predictions. This allows us to analyze the model's performance across different classes and identify any Patterns or discrepancies.

Additionally, we can explore the top losses, which are images that the model struggled with the most during training. These top losses can provide insights into the challenges the model faces and help us fine-tune the training process or clean the dataset if necessary.

Data Cleaning and Augmentation with Fast AI

To improve the model's performance and generalization capabilities, we can leverage data augmentation and cleaning techniques provided by Fast AI. Data augmentation involves applying various transformations to the training images, creating additional variations and increasing the model's exposure to different scenarios.

Fast AI offers built-in functions for common data augmentation techniques like rotation, flipping, brightness adjustments, and more. By including batch transformations during the data loading phase, we can apply these transformations to batches of images, further enhancing the model's ability to generalize.

Additionally, Fast AI provides tools for cleaning the dataset, allowing us to remove corrupt or irrelevant images. By using the ImageClassifierCleaner widget, we can interactively review the dataset and make decisions on which images to keep, delete, or re-label. This iterative process ensures that our dataset is clean and free from anomalies, contributing to better model performance.

Exporting and Saving the Trained Model

Once our model is trained and we are satisfied with its performance, we can export and save it for future use and deployment. Fast AI allows us to export both the architecture of the model (neural network structure) and the learned parameters (weights).

By using the export function, we can specify the path where we want to save the model and ensure that the exported file has the .pkl extension. This pickled file format stores all the necessary information for reproducing the model's behavior.

Saving the trained model allows us to reuse it in other applications or share it with colleagues and collaborators. The exported model can be loaded and used for making predictions on new, unseen data.

Loading the Saved Model and Making Predictions

To utilize the saved model for making predictions, we need to load it back into our environment. Fast AI provides the load_learner function, which allows us to load the exported model just by providing the saved path.

Once the model is loaded, we can use it to make predictions on new images or datasets. By invoking the predict function of the model object and passing the image, we can obtain the predicted label and the corresponding probability scores for each class.

With the loaded model, we can seamlessly integrate it into other applications or use it for further analysis and decision-making.

Creating an Interactive Voila Application

Fast AI offers the ability to create interactive applications using the "Voila" library. This allows us to deploy our models as standalone web applications, allowing users to interact with the model and obtain predictions easily.

By leveraging the Voila library and integrating it with Fast AI, we can create dynamic user interfaces for our trained models. These interfaces can include features such as file upload buttons, classification buttons, output areas for displaying predictions, and more.

With an interactive Voila application, we can provide a user-friendly interface for individuals to interact with our deep learning models without the need for coding knowledge. This makes deploying and sharing our models more accessible and user-friendly.

Deploying the Voila Application with Binder

To make our Voila application accessible over the internet, we can use the Binder platform for deployment. Binder allows us to host our Jupyter notebooks containing the Voila applications and share them easily.

To deploy our application with Binder, we need to follow a few simple steps. These include adding our notebook to a repository, inserting the repository URL into the Binder URL field, specifying the rendering method, and launching the notebook.

Once launched, our Voila application will be live and accessible via the provided URL. Users can Interact with the application, upload images, and receive predictions from our trained model.

By deploying our models with Binder and Voila, we can share our work with others and allow them to experience the power of Fast AI and deep learning firsthand.

Highlights:

1. Fast AI is a research institute founded by Jeremy Howard and Sylvia Rachel Thomas, dedicated to making deep learning accessible to all levels of practitioners.
2. Fast AI provides a user-friendly and streamlined workflow for deep learning, from data loading to model training and deployment.
3. The library offers pre-trained models, data augmentation and cleaning tools, interactive visualization features, and deployment capabilities.
4. Fast AI's Data Block API allows for flexible data loading and transformation, making it easy to work with different kinds of datasets.
5. The ResNet architecture is a powerful and widely-used model in Fast AI, enabling transfer learning for various image classification tasks.
6. Evaluation of model performance is facilitated through confusion matrices, top losses analysis, and data cleaning techniques.
7. Fast AI supports exporting and saving trained models for future use and sharing with others.
8. Voila, a library integrated with Fast AI, enables the creation of interactive web applications for deployment.
9. Binder provides a platform to host and deploy Voila applications, making them accessible over the internet.

FAQ

Q: Can Fast AI be used by beginners in deep learning?

A: Yes, Fast AI is specifically designed to be beginner-friendly and provides a smooth learning curve for individuals new to deep learning.

Q: What types of data can be loaded and processed in Fast AI?

A: Fast AI supports various data types, but it excels in handling image datasets using the Data Block API. However, it can also be used for text and tabular data.

Q: Can Fast AI be used for other tasks beyond image classification?

A: Yes, Fast AI offers a range of functionalities and models beyond image classification. It supports tasks such as object detection, semantic segmentation, and more.

Q: Is it necessary to have a GPU for using Fast AI?

A: While having a GPU can significantly speed up the training process, Fast AI can also be used with CPUs. However, training times may be significantly longer without a GPU.

Q: Can Fast AI be used for real-world applications?

A: Absolutely! Fast AI enables the development and deployment of models for real-world applications. Its robustness and user-friendly nature make it suitable for various use cases.

Q: Can models trained with Fast AI be deployed on a cloud server?

A: Yes, models trained with Fast AI can be easily deployed on cloud servers and integrated into web applications or APIs.

Q: Does Fast AI support TensorFlow or other deep learning frameworks?

A: Fast AI primarily uses PyTorch as its deep learning framework. However, it also provides compatibility with TensorFlow through the fastai.vision.learner package.

Q: Is Fast AI suitable for advanced deep learning practitioners?

A: Yes, Fast AI caters to all levels of deep learning practitioners, including those with expertise in the field. Its flexible and powerful toolkit allows for complex model architectures and customizations.

Q: Are there any commercial or enterprise applications of Fast AI?

A: Fast AI can be utilized in various commercial or enterprise applications, ranging from image recognition in e-commerce to anomaly detection in finance.

Q: Can Fast AI be used with other Python libraries and frameworks?

A: Yes, Fast AI can be used in conjunction with other Python libraries and frameworks. It provides extensive integration capabilities and works well with popular data science tools.