You need to agree to share your contact information to access this dataset

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

This dataset is released for research use and access is reviewed and granted manually by the maintainers. Licensing of the underlying source is NOT yet cleared (see the card). Please state your name, affiliation, and intended use.

Log in or Sign Up to review the conditions and access this dataset content.

AM09_DED_energy

DED coaxial melt-pool frame -> laser-energy level P1-P4 (4-class image classification). Category B, task T-B2, in the unified Smart-Manufacturing SFT schema.

Records

62,194 records (train=62,194). Model input: a single coaxial VIS melt-pool image (590x480), bytes embedded in the image column.

Unified SFT schema (7 fields)

field type meaning
query str the question / instruction (model input)
image Image | null the INPUT image (bytes embedded) — null for tabular records
annot str the answer — for this dataset: the energy-level label — one of P1,P2,P3,P4 (text)
reasoning null no native CoT in this dataset
cate "B" SFT category
task "T-B2" unified task id
metadata str (JSON) split, provenance, units, input, license, and (for AM11/13) taxonomy_note

Notes

Uploaded as ONE dataset (all rows together); the original train/test partition lives in metadata.split (train 43,463 / test 18,731), NOT as HF splits. The label is the source folder (P1-P4), verified by viewing pixels (P1 small/dim ... P4 large + halo).

Provenance & licensing

Underlying source: DED coaxial melt-pool imaging, Zenodo DOI 10.5281/zenodo.10421423. Upstream license: CC-BY-SA-4.0 (ShareAlike/viral) + CC-BY-4.0 (mixed on Zenodo). Converted read-only into the unified schema; conversion + publish scripts live in AI4Manufacturing/forge_model (AM09_DED_energy/ + publish/push_am_to_hf.py). Access is gated (manual approval); clear the upstream licence before any onward redistribution.

Downloads last month
9