bobholamovic/dudulearnstocode-template

This is a starter template for deep learning projects, especially computer vision tasks like image recognition or object detection. Think of it as a pre-organized folder structure with some basic plumbing already in place, so you can jump straight into training your model instead of spending hours setting up boilerplate code. Built on PyTorch, a popular open-source toolkit for machine learning, the template aims to strike a balance between two common working styles. Researchers often prefer configuring things, tweaking settings, running experiments, and focusing on results. Developers tend to favor writing clean, structured code that is easy to maintain and extend. The project's vision is to support both approaches without forcing you into one mold. It deliberately stays lightweight, avoiding the heaviness of a full framework while still giving you more structure than a blank slate. Someone starting a new computer vision experiment would use this. For example, a graduate student building an image classifier for a research paper, or a startup founder prototyping a product that recognizes defects on a manufacturing line. Instead of copying and pasting setup code from old projects or tutorials, they begin with this scaffold and focus on their actual model and data. The README is sparse on specifics, so it does not go into detail about exactly what files are included or how the project is organized. What is notable is the philosophy: rather than locking you into a framework's conventions, it offers a flexible starting point you can adapt. The tradeoff is that with minimal documentation, you need some familiarity with PyTorch to get the most out of it. The project currently has a placeholder name and few stars, suggesting it is an early-stage personal project shared in case others find the structure useful.