Kornia VS Elixir-Code-Smells

👉 https://github.com/kornia/kornia/releases/tag/v0.6.6

Kornia: Differential Computer Vision\ (3 comments)

Kornia - Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.

Between "not being sure yet" about GPU operations in pre-processing and choosing high-end CPUs, I think you are overthinking the wrong alternative. Besides DALI, check whether you are using codecs besides nvidia/torchvision-supported jpeg and png, and if other GPU CV libraries meet your needs: torchvision kornia

Use kornia.augmentation where this problem is solved doing the augmentations in batch outside the dataloader. https://github.com/kornia/kornia

My Ph.D. advisor and I are researching code smells in Elixir systems. This work resulted in the Catalog of Elixir-specific code smells, which has already become quite popular among GitHub users. (https://github.com/lucasvegi/Elixir-Code-Smells).

Results can be found in: https://github.com/lucasvegi/Elixir-Code-Smells

Catalog of Elixir-specific code smells\ (21 comments)

Generally speaking, code smells are also context-sensitive structures. This means that a smell will not always represent pain in a specific project. It is just a possibility that must be analyzed in each context. It is a warning, not a judgment.

We are interested in knowing Elixir's community opinion about these code smells. Collaborations can be made via GitHub issues (https://github.com/lucasvegi/Elixir-Code-Smells/issues) or Pull Requests in our repo!