How PyTorch’s CosineEmbeddingLoss works to push similar embeddings closer and dissimilar ones apart using cosine distance and a margin threshold
Workaround for Pydantic v1 post-init logic using properties and custom init overrides
How to choose a small, high-accuracy timm backbone for transfer learning by filtering ImageNet results by accuracy and parameter count
Using PyTorch Lightning’s built-in learning rate finder to plot loss vs learning rate and pick an optimal value
A simple weighted cross-entropy loss function as an intuitive alternative to focal loss for handling imbalanced classification
How to prepare and transform image data for segmentation
