Mastering Image Segmentation with PyTorch

Leverage tensors and computational graphs to compute automatic gradients with PyTorch. Follow forward and backward passes, apply the chain rule, and update weights via gradients.

Set up a Python environment in vscode, import torch and numpy, create tensors with requires_grad, compute gradients with backward on a simple neural-network-like graph, and visualize results with Seaborn.

Build your first neural network from scratch using tensors and a linear regression setup on car weight and mpg, trained over epochs with gradient updates.

Verify a linear regression from scratch by inspecting weights and bias, plotting predictions versus true values, and matching results to a statistical linear regression model.

Construct and instantiate a PyTorch model class that inherits from nn.Module, implement init and forward, and train with mean squared error loss and SGD, including gradient updates and epoch-based visualization.

Experiment with learning rate and number of epochs to train the model and observe when results stabilize. Practice evaluating results to discover effective parameter combinations, before reviewing my solution.

Explore how learning rate and epochs interact, using manual tuning and losses plots, with a preview of grid search for systematic hyperparameter testing.

Partition large datasets into batches to train efficiently within memory constraints, balancing training speed and learning stability; typical batch sizes range from 1 to 512, with 32 as a default.

Define a batch size, loop over epochs, and train a PyTorch model by slicing data into batches from i to i+batch_size, matching predictions to true values.

Explore datasets and dataloaders in PyTorch, learn to build custom datasets with init, len, and get item, and separate model training from data processing with batched, shuffled data.

Build a dataset by implementing the init, length, and get item functions, then create a data loader with a batch size and integrate it into the training loop.

Save and load PyTorch model parameters by using state_dict, saving to a file, then reinitializing the model and loading the state dict to resume training or inference.

Explore the standard training loop for PyTorch models, including forward passes, loss calculation, backward propagation, and optimizer steps, and how model parameters, the optimizer, and gradients interact.

Master hyperparameter tuning to optimize training and inference time, stability, and performance by adjusting topology, learning rate, batch size, and epochs, using grid search, random search, and Scorch.

Execute hyperparameter tuning via grid search to optimize learning rate and max epochs for a PyTorch model using squash, evaluate combinations with cross validation, and identify the best parameters.

Explore convolutional neural networks (cnn) and how they detect local patterns with convolutional filters, feature maps, stride, and pooling, enabling translational invariance in computer vision models.

Explore convolutions interactively using convolutional filters to visualize edge detection, sharpening, and blurring on grayscale images, and understand the math behind neighboring pixel interactions.

Apply image preprocessing for PyTorch by resizing, center cropping, greyscale conversion, rotations, random vertical flips, tensor conversion, and normalization within a composed augmentation pipeline.

Learn image preprocessing in a PyTorch workflow, including resizing to fixed dimensions, random rotation and crop, grayscale conversion, random flips, tensor conversion, and normalization with mean and standard deviation.

Mastering image segmentation with PyTorch covers tensor dimensions from batch size to height and width, and explains 1d, 2d, and 3d convolutions plus converting conv outputs to fully connected layers.

Explore debugging a PyTorch convolutional network by building a sequential model, feeding a correctly shaped random input, and inspecting conv, pooling, flatten, and linear layers to verify shapes.

Explore architectures for semantic segmentation, focusing on encoder–decoder designs with contracting and expansive paths, skip connections, and multi-level feature maps via a top-down pathway.

Explore upsampling in image segmentation by increasing resolution after an encoding path, comparing nearest neighbor, linear interpolation, and transposed convolutions, and examine checkerboard artifacts.

Explore loss functions for image segmentation, comparing the dice coefficient and soft dice loss with pixel-wise cross-entropy loss, and how they handle overlap with ground-truth masks and class balance.

Explore evaluation metrics for image segmentation, including intersection over union (IoU) and pixel accuracy, with examples of true positives, false positives, and false negatives in ship detection.

Learn data preparation for image segmentation: organize images and masks into train, validation, and test folders, and create small patches from large images for aligned input and mask pairs.

Develop data prep for semantic segmentation by creating train, val, and test folders with images and masks, using Python scripting and folder checks to ensure a fresh setup.

Create a function to generate 320 by 320 patches from a source image, validating color channels, and save patches to a destination path with tile numbers extracted from the path.

Iterate over all data files to generate patch images for training, validation, and testing by pairing images with masks, checking file existence, and converting arrays to images before saving.

Set up a PyTorch training loop for image segmentation, track train and validation losses across epochs, perform forward passes and backpropagation, and update the model with the optimizer.

Plot train and validation losses across epochs with seaborn. Save the model’s state dictionary in a models folder with an epoch-based filename for test data inference.

Test the segmentation model on the test data, perform inference, and evaluate with pixel accuracy and IOU metrics using Torch Metrics.

Master image segmentation with PyTorch by evaluating model predictions on test images, comparing true and predicted masks, and analyzing metrics like pixel accuracy and intersection over unions.