Tutorials
=========

PyImgAno is a broad toolkit, and users typically start from one of these tracks:

1) **Classic anomaly detection** on feature vectors (fast, CPU-friendly).
2) **Industrial visual anomaly detection** on images (pixel maps, tiling, JSONL inference).

Recommended starting points
---------------------------

- Industrial workflow (train → export infer-config → infer):
  https://github.com/skygazer42/pyimgano/blob/main/docs/WORKBENCH.md

- Inference modes (numpy-first, tiling, defects export):
  https://github.com/skygazer42/pyimgano/blob/main/docs/INDUSTRIAL_INFERENCE.md

- CLI reference:
  https://github.com/skygazer42/pyimgano/blob/main/docs/CLI_REFERENCE.md

- Benchmark reports + metrics:
  https://github.com/skygazer42/pyimgano/blob/main/docs/EVALUATION_AND_BENCHMARK.md
