Datasets:

Alex-Ultra
/

3d-cli-print-failure-frames

license: cc-by-4.0
task_categories:
  - image-classification
tags:
  - 3d-printing
  - fdm
  - print-failure
  - snapmaker-u1
size_categories:
  - n<1K
pretty_name: 3d-cli print-failure frames

3d-cli print-failure frames

Camera frames from two FDM printers (Snapmaker U1, Creality K1 SE), labelled for the tier (b) whole-frame vision model of 3d-cli (3d printer vision; code: tools/vision_train). Classes: healthy, spaghetti, part_detached, empty_bed.

This set is small and unbalanced. 421 healthy frames from printing and a handful of failure frames (7 empty_bed, 1 part_detached, 7 spaghetti). It is meant to be extended with frames recorded by 3d printer vision record; it is not enough to train a detachment detector on its own. part_detached is one event (a large honeycomb panel that peeled off the plate): do not read a metric computed on it as evidence.

Files

own/frames/<id>.jpg: sanitised frames (<id> = first 16 hex of the file's sha256).
labels.jsonl: id, file, label, split (train|test), group (one print or event: keep a group on one side of a split), printer, source, width, height, sha256, qr_masked.
sanitation.json: per source how many frames were found, published, refused or masked, and the tool versions. DATASET_SHA256: sha256 over the sorted id|sha256|label|split lines of the published rows (the id of the dataset revision a model card pins).

Sanitation

Frames were decoded and re-encoded (EXIF/ICC removed), cropped to the printer's bed-and-part area (rooms, windows, screens and the chamber's back wall with its QR stickers are outside the crop), QR codes were painted over, and a face and person detector (OpenCV Haar + HOG) refused any frame with a hit (36 frames refused). File names carry no timestamps or project names. The automatic checks are no proof: contact sheets of every published frame were reviewed by a human before publishing. If you find a frame that should not be here, open an issue on the GitHub repo.

Licence and provenance

All frames are the owner's own captures: CC-BY-4.0. The prints on the bed are the owner's own models and models downloaded from model sites; no model files are included. The public frame collections used for evaluation next to this set (spaghetti, bed-adhesion failures, ...) carry their own licences and are not redistributed here; their provenance is documented in the 3d-cli repository (docs/research/printer-vision-tuning.md).

Revision

Dataset sha256 306bd93d73a5dfc7d966dfb6e230b2247ee6da5c42a6ecf0f62213b37791fc8e; built by tools/vision_train/dataset.py at 3d-cli commit a06ced7554e6d172844166675447f3e2d26c7e8a.