Datasets:
Add frames (dataset sha256 306bd93d73a5dfc7)
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- README.md +54 -0
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DATASET_SHA256
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README.md
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---
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license: cc-by-4.0
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task_categories:
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- image-classification
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tags:
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- 3d-printing
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- fdm
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- print-failure
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- snapmaker-u1
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size_categories:
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- n<1K
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pretty_name: 3d-cli print-failure frames
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---
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# 3d-cli print-failure frames
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Camera frames from two FDM printers (Snapmaker U1, Creality K1 SE), labelled for the tier (b)
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whole-frame vision model of [3d-cli](https://github.com/alex-mextner/3d-cli) (`3d printer vision`;
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code: `tools/vision_train`). Classes: `healthy`, `spaghetti`, `part_detached`, `empty_bed`.
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**This set is small and unbalanced.** 421 healthy frames from printing and a handful of
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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
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not enough to train a detachment detector on its own. `part_detached` is one event (a large
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honeycomb panel that peeled off the plate): do not read a metric computed on it as evidence.
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## Files
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- `own/frames/<id>.jpg`: sanitised frames (`<id>` = first 16 hex of the file's sha256).
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- `labels.jsonl`: `id, file, label, split (train|test), group (one print or event: keep a group
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on one side of a split), printer, source, width, height, sha256, qr_masked`.
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- `sanitation.json`: per source how many frames were found, published, refused or masked, and the
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tool versions. `DATASET_SHA256`: sha256 over the sorted `id|sha256|label|split` lines of the
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published rows (the id of the dataset revision a model card pins).
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## Sanitation
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Frames were decoded and re-encoded (EXIF/ICC removed), cropped to the printer's bed-and-part area
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(rooms, windows, screens and the chamber's back wall with its QR stickers are outside the crop),
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QR codes were painted over, and a face and person detector (OpenCV Haar + HOG) refused any frame
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with a hit (36 frames refused). File names carry no timestamps or project names. The
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automatic checks are no proof: contact sheets of every published frame were reviewed by a human
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before publishing. If you find a frame that should not be here, open an issue on the GitHub repo.
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## Licence and provenance
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All frames are the owner's own captures: CC-BY-4.0. The prints on the bed are the owner's own
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models and models downloaded from model sites; no model files are included. The public frame
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collections used for evaluation next to this set (spaghetti, bed-adhesion failures, ...) carry
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their own licences and are **not** redistributed here; their provenance is documented in the
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3d-cli repository (docs/research/printer-vision-tuning.md).
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## Revision
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Dataset sha256 `306bd93d73a5dfc7d966dfb6e230b2247ee6da5c42a6ecf0f62213b37791fc8e`; built by `tools/vision_train/dataset.py` at 3d-cli commit `a06ced7554e6d172844166675447f3e2d26c7e8a`.
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