Mastering Image Segmentation with PyTorch

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.

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.

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.

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.

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.

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.

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.