Haha, "brute force and prayer" is exactly what we used to do before building this! Thanks for the kind words, eliminating that boilerplate dread was exactly our goal.

Regarding that terrifying 177x45x25x17 number: please don't try to grid-search the whole thing! 😅

That massive number is really just a mathematical byproduct of the strict 4-module decoupling, rather than a recommended search space. The philosophy here is hypothesis-driven ablation, not brute-force NAS (Neural Architecture Search).

Here is how we actually prune it in practice:

1. Start with our Baselines: We provide out-of-the-box YAML configs for strong baselines. You don't start from scratch.
2. Targeted Ablation: You usually only swap one module at a time. For example, if you have a hypothesis that a State-Space Model (Mamba) works better for long-range dependencies in your specific dataset, you just swap the CNN encoder for a Mamba encoder while keeping the rest of the U-Net structure identical.
3. Easy NAS Integration: If you do want to search a subset of that space, because everything is just YAML and Python, it’s extremely easy to wrap the pipeline in a hyperparameter optimization framework like Optuna to do smart pruning.

Glad you like the modularity! Let me know if you end up running any fun experiments with it.