Skip to content

v0.13.0 — spaghetti training pipeline + trained model

Choose a tag to compare

View all tags
@tholterhus tholterhus released this 04 Oct 16:20
· 55 commits to main since this release
v0.13.0
5d27939

A ready-to-use failed-print model for the pycentauri detector.

Do you even need these files?

Probably not. The trained models are bundled with the package — a pip install from this repository or a plain git clone includes both the Coral and the CPU variant plus the labels, and DETECT_MODEL=auto (the default) finds them automatically. No manual download needed.

These assets exist for standalone use: if you install differently, want to copy the model to another machine, or inspect it.

Which files are which?

  • No Coral stick? You only need spaghetti.tflite (the CPU model — runs on any machine, ~200 ms per look) and spaghetti.txt (the label file, found automatically by its name). Point DETECT_MODEL at data/models/spaghetti.tflite.
  • Coral stick? Use spaghetti_edgetpu.tflite (the stick's AI chip does a look in ~5–15 ms) — and keep spaghetti.tflite next to it, so a machine without the stick still works.

Everything else is optional: spaghetti-float32.tflite (unquantized variant) and model-result.zip (the raw export bundle) only matter if you want to inspect or retrain.

Install (only if you don't use pip/git)

Copy the files you need into data/models/ in your pycentauri directory (a model there wins over the bundled one), then:

# in /etc/pycentauri.conf:
DETECT_MODEL=data/models/spaghetti_edgetpu.tflite   # Coral
# or: DETECT_MODEL=data/models/spaghetti.tflite     # CPU-only machine
# or simply: DETECT_MODEL=auto

# then verify:
centauri detect check
# → backend: edgetpu (Coral) or cpu, ~5-15 ms or ~200 ms per look

Details: README — Spaghetti detection · How the model was made and how to retrain: scripts/train/README.md

Where the model comes from (license attribution)

Trained on 7,295 images / 13,791 boxes:

  • "3D Printer Spaghetti Detection" dataset by training-l9tjj (Roboflow Universe) — Public Domain
  • "Spaghetti" dataset by aiot-innowork (Roboflow Universe) — CC BY 4.0 (modified: only the spaghetti class subset used)
  • Base architecture: SSD MobileNet V2 320×320, TensorFlow Object Detection API — Apache-2.0

It detects one failure mode: spaghetti (a print that has come loose and turned to stringy mess). It is a runaway-print alarm, not a quality inspector — warping/zits/layer shifts need the planned 4-class model.

After installing, run one print in notify mode (zero false alerts) before arming pause/stop in the dashboard.

Assets 7
Loading