Mastering Image Segmentation with PyTorch using Real-World Projects

1. Image Segmentation (101)

In this video, we will learn what image segmentation is, which different kinds of image segmentations are available, and what we are going to develop in this course.

2. Course Scope

In this video, we will explore the comprehensive scope of the course, starting with an overview of the material, setup instructions, and installation of required packages.

3. System Setup

In this video, we will explore the essential steps for setting up our system for the course.

4. How to Get the Material

In this video, we will learn how to access the course materials hosted on GitHub. We will discover two methods: cloning the repository using GitHub or downloading and extracting the files directly. Check out the GitHub repositories at https://github.com/PacktPublishing/Mastering-Image-Segmentation-with-PyTorch

5. Conda Environment Setup

In this video, we will learn how to set up a coding environment for working with PyTorch and image segmentation tasks. The video covers creating a virtual environment, installing required packages such as PyTorch, OpenCV, and segmentation models, and ensuring that the environment is configured correctly for interactive coding.

1. Modelling Section Overview

In this video, we will receive a brief overview of the modeling section, giving us a clear expectation of its contents.

2. PyTorch Introduction (101)

In this video, we will explore the fundamentals of PyTorch, a powerful deep learning framework. Discover what PyTorch is, its ease of use, diverse applications including regression, classification, NLP, and reinforcement learning. Learn why PyTorch's simplicity, strong community, and compatibility with GPUs make it an excellent choice for deep learning development.

3. Tensor Introduction

In this video, we will delve into the world of tensors, the fundamental building blocks of PyTorch. Learn about tensor creation, manipulation, and their interlinking. Discover the crucial concept of autograd and its connection to tensors, as we lay the groundwork for understanding PyTorch's core operations.

4. From Tensors to Computational Graphs (101)

In this video, we will explore the fundamental concepts of tensors and automatic gradients using PyTorch. We will learn how tensors form the core structure for working with variables and how they are more powerful than NumPy arrays due to their ability to calculate gradients automatically. The video covers forward and backward passes in a simple neural network, demonstrating how weights are updated based on gradients.

5. Tensor (Coding)

In this video, we will explore tensors and gradients in PyTorch. We start by creating tensors and applying simple calculations. Then, we delve into calculating gradients for functions and nodes in a computational graph. This foundational knowledge sets the stage for understanding neural networks and deeper machine learning concepts.

6. Linear Regression from Scratch (Coding, Model Training)

In this video, we will create our first neural network model from scratch using PyTorch. We will cover data conversion, initialization of weights and biases, forward and backward passes, loss calculation, and parameter updates through a simple linear regression example.

7. Linear Regression from Scratch (Coding, Model Evaluation)

In this video, we will explore the process of creating a neural network for linear regression. We will start by examining the model's parameters, including weights and bias. Through Python code examples, we will calculate predictions and visualize the regression line. The video demonstrates how to create a simple neural network, compares its results with statistical methods, and offers insights into building more complex networks in future videos.

8. Model Class (Coding)

In this video, we will learn how to create a linear regression model using PyTorch. The video covers setting up the model using a separate class, defining the initialization and forward functions, calculating losses, and optimizing the model using stochastic gradient descent. We will also explore the importance of calculating gradients for proper training.

9. Exercise - Learning Rate and Number of Epochs

In this exercise video, we will delve into the concept of hyperparameters in machine learning.

10. Solution - Learning Rate and Number of Epochs

In this solution video, we will explore the intricate relationship between learning rate and the number of epochs in machine learning. Through practical experimentation and analysis, we will decipher how these parameters impact training stability and convergence, shedding light on the delicate balance required for optimal results.

11. Batches (101)

In this video, we will delve into the concept of data batching in machine learning. We will learn why partitioning our dataset into smaller batches is essential for efficient training, the impact of different batch sizes on model performance and training stability and discover typical best practices for selecting the optimal batch size.

12. Batches (Coding)

In this video, we will dive into the practical implementation of batch sizes in linear regression using PyTorch. Building upon the concepts learned in the previous video, we will explore how to iteratively train a linear regression model by passing smaller batches of data through each epoch, enhancing the efficiency of model training.

13. Datasets and Dataloaders (101)

In this video, we will delve into the concepts of datasets and dataloaders in PyTorch. We will learn why creating these classes is crucial for separating data processing from model training, leading to improved code modularity and readability. The video covers implementing custom dataset classes, handling data loading, and explores the functionality of dataloaders.

14. Datasets and Dataloaders (Coding)

In this video, we will explore how to create a custom dataset class and dataloader in PyTorch for training a machine learning model. The video covers implementing the necessary functions, including __init__, __len__, and __getitem__, within the dataset class. It also demonstrates how to utilize the enumerate function and the dataloader to efficiently iterate through batches of data during the training process. The video emphasizes the importance of structuring the data effectively for training purposes.

15. Saving and Loading Models (101)

In this video, we will explore the process of efficiently saving and loading deep learning models in PyTorch. Learn why model saving is crucial, how to use the state dictionary for effective storage, and why saving the complete model is not recommended. Discover the recommended practices for saving and loading models to ensure compatibility and streamline the workflow.

16. Saving and Loading Models (Coding)

In this video, we will dive into the process of effectively saving and loading trained deep learning models using PyTorch. Explore how to save model states, access the state dictionary, and load models for future use. Learn the practical steps to ensure seamless model preservation and retrieval.

17. Model Training (101)

In this video, we will explore the intricacies of training PyTorch models. The video covers various components such as the model, optimizer, loss function, gradient updates, and the training loop structure. The discussion delves into how these components interact and impact each other during the training process.

18. Hyperparameter Tuning (101)

In this video, we will explore the concept of hyperparameter tuning. The video covers the importance of tuning parameters for training and inference times, improving results, and ensuring model convergence. It delves into various hyperparameters such as network topology, batch size, number of epochs, hidden layer configuration, and more. The video discusses techniques such as grid search and random search for structured parameter exploration.

19. Hyperparameter Tuning (Coding)

In this video, we will learn how to perform hyperparameter tuning using grid search. The video covers setting up the necessary packages, loading data, and implementing grid search using the "skorch" package to find the best parameter combination for a neural network model. The process involves evaluating different parameter combinations and selecting the one with the best score. The video demonstrates the steps to set up the grid search, perform cross-validation, extract the best score, and identify the optimal parameter combination.

1. CNN Introduction (101)

In this video, we will explore the fundamentals of Convolutional Neural Networks (CNNs). We will learn how CNNs are used in computer vision tasks, how they detect local patterns, and their ability to handle translational invariance. The video also covers key concepts such as convolutional filters and pooling layers, which are essential for understanding CNN architecture.

2. CNN (Interactive)

In this video, we will explore the concept of convolutions. We will learn how convolutional filters affect images, delve into the mathematics behind them, and see practical examples of methods such as sharpening and blurring. Gain a deeper understanding of this essential image processing technique.

3. Image Preprocessing (101)

In this video, we will explore image preprocessing techniques, including resizing, cropping, grayscale conversion, rotation, vertical flipping, tensor conversion, and normalization. These techniques are crucial for preparing images for machine learning models.

4. Image Preprocessing (Coding)

In this video, we will explore the process of image preprocessing for machine learning applications. The video covers resizing images, random rotations, center cropping, grayscale conversion, and normalization. It also demonstrates how to extract the mean and standard deviation of an image. This comprehensive guide provides valuable insights into enhancing image data for better model training.

5. Layer Calculations (101)

In this video, we will explore the concept of layer calculations in Convolutional Neural Networks (CNNs). The video covers the problem of debugging CNNs, understanding tensor dimensions, and the interaction between different layers in a CNN.

6. Layer Calculations (Coding)

In this video, we will explore the process of debugging neural networks, starting with layer calculations. The video covers topics such as importing packages, creating random input data with the same shape as the image, and checking the output at different stages of the network. It demonstrates how to understand the dimensions and structures of data as it passes through convolutional and pooling layers, helping viewers gain insights into debugging and building neural networks effectively.

1. Architecture (101)

In this video, we will explore various neural network architectures for semantic segmentation. The video covers topics such as the UNet architecture, skip connections, and the Feature Pyramid Network (FPN). These architectures are crucial for improving image analysis and understanding.

2. Upsampling (101)

In this video, we will explore the concept of upsampling in the context of neural networks. We will discuss how upsampling techniques can help restore the resolution of images that have undergone downsampling, such as through pooling layers. Various methods such as nearest neighbor, bilinear interpolation, and transposed convolutions will be explained.

3. Loss Functions (101)

In this video, we will explore two different loss functions used in image segmentation: the Dice coefficient and the pixel-wise cross-entropy loss. These functions are essential for evaluating the performance of segmentation models, measuring similarity, and ensuring accurate predictions.

4. Evaluation Metrics (101)

In this video, we will delve into crucial evaluation metrics for object detection models. We will explore "Intersection Over Union (IoU)", a metric that assesses the accuracy of bounding box predictions, and "Pixel Accuracy", which measures the accuracy of pixel-level predictions.

5. Coding Introduction (101)

In this video, we will dive into the development of a semantic segmentation algorithm for satellite images of Dubai. We will explore the dataset, consisting of six different classes, and prepare the data for modelling.

6. Data Prep Introduction (101)

In this video, we will focus on data preparation for image analysis. We will explore folder structures, understand the task at hand, and learn about the challenges of handling images of different sizes.

7. Data Prep I - Create Folders (Coding)

In this video, we will learn how to create folders for organizing image and mask data. We will use Python and various packages such as OS, regex, and pathlib to automate the process. The video covers creating folders for training, validation, and testing datasets.

8. Data Prep II - Patches Function (Coding)

In this video, we will learn how to create patches from a source image. The video covers the process of splitting the source image into patches, extracting the tile number from the file path, and saving the individual patches to a destination folder. This video is useful for image processing tasks that require dividing an image into smaller sections.

9. Data Prep III - Create All Patch-Images (Coding)

In this video, we will learn about data preparation, specifically focusing on iterating over different files within subfolders. The video covers how to work with file paths, extract filenames, and organize data into training, validation, and test sets based on tile numbers. This preparation sets the stage for the subsequent steps in the video series.

10. Modelling - Dataset (Coding)

In this video, we will learn how to set up a custom dataset class for image segmentation. The video covers creating a class to handle image and mask data, ensuring pairs of images and masks, and loading and formatting the data for machine learning.

11. Modelling - Model Setup (Coding)

In this video, we will set up the foundational elements for training a model, including defining hyperparameters, creating dataloaders, setting up the model architecture, optimizer, and loss criterion. This video is part of a series on model training.

12. Modelling - Training Loop (Coding)

In this video, we will learn how to set up a training loop for a machine learning model. The video covers specifying training and validation losses, iterating over epochs, performing forward passes, calculating losses, and updating weights. The video provides a detailed walkthrough of the training process.

13. Modelling - Losses and Saving (Coding)

In this video, we will learn how to set up model training, perform the training loop, check validation results, monitor training and validation losses, and save the trained model in a Python file.

14. Model Evaluation - Calc Metrics (Coding)

In this video, we will learn how to test a machine learning model and perform model inference. The video covers topics such as importing necessary packages like NumPy, Torch, and Torch metrics, setting up a dataset and dataloader, loading model weights, and evaluating the model's performance using pixel accuracy and Intersection over Unions (IoU). The video also demonstrates how to calculate the number of correct pixels and pixel accuracy.

15. Model Evaluation - Check Prediction (Coding)

In this video, we will explore model inference and evaluate its performance on a test dataset. We load the trained model's weights and apply it to randomly selected test images, comparing the true masks with the predicted masks. While there is room for improvement, this initial evaluation shows promising results.