datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
vqa-meltpool-dataset
NIR Meltpool Thermal-State VQA
A visual-question-answering dataset for training vision-language models to classify
the thermal state of a laser powder-bed-fusion (LPBF) meltpool from a single
near-infrared (NIR) frame. Derived from the hyperspectral_nir_meltpool_dataset
(sample 588).
Task
Given one 250x250 NIR meltpool image, answer the fixed query by choosing one of
six thermal-state classes.
id
label
id
label
0
baseline
3
strong underheat
1
edge
4… See the full description on the dataset page: https://huggingface.co/datasets/MoreGeometrico/vqa-meltpool-dataset.AM12_MeltPoolKinetics
Roles
Roles: canon repo — annot is the source label (normal / underheat / overheat / edge for the 4-class question, normal / anomaly for the binary one), kept machine-parseable as the gold for verification and reward parsing; the model reads query + image, and metadata.output_type says which of the two questions a record asks. The reasoning column is empty, so this repo is not itself a training view for chain-of-thought. metadata is provenance and must never be fed to a model.… See the full description on the dataset page: https://huggingface.co/datasets/AI4Manufacturing/AM12_MeltPoolKinetics.AM12_MeltPoolKinetics-perception
Roles
Roles: canon repo — annot is the source label (normal / underheat / overheat / edge for the 4-class question, normal / anomaly for the binary one), kept machine-parseable as the gold for verification and reward parsing; the model reads query + image, and metadata.output_type says which of the two questions a record asks. The reasoning column is empty, so this repo is not itself a training view for chain-of-thought. metadata is provenance and must never be fed to a model.… See the full description on the dataset page: https://huggingface.co/datasets/AI4Manufacturing/AM12_MeltPoolKinetics-perception.melt-pool-classificationAM13_MeltpoolNet-annotated
AM13_MeltpoolNet — CoT-annotated (T-AM2)
Chain-of-thought SFT annotation of the T-AM2 melt-pool-mode task from AM13_MeltpoolNet.
Each row: an LPBF process record (query), the gold melt-pool mode (annot), and a worked
physical reasoning chain ending in FINAL ANSWER: <mode>.
Source (un-annotated, 181-style queries): AI4Manufacturing/AM13_MeltpoolNet.
Teacher: claude-sonnet-5 (gold-conditioned rationalization; each reasoning derived from the
record's raw values, with error-prone… See the full description on the dataset page: https://huggingface.co/datasets/AI4Manufacturing/AM13_MeltpoolNet-annotated.AM13_MeltpoolNet
AM13_MeltpoolNet
Unit-normalized process/material inputs -> source-native 5-way melt-pool mode classification. Tabular. Category C, task T-AM2, in the unified Smart-Manufacturing SFT schema.
Records
1,236 records (train=1,236). Model input: tabular — no image.
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… See the full description on the dataset page: https://huggingface.co/datasets/AI4Manufacturing/AM13_MeltpoolNet.Meltpools
Dataset card for MeltPoolChordsSingleCelled
hand labeled meltpools
This dataset was created using Segments.ai. It can be found here.
Dataset categories
Id
Name
Description
1
chord
-
2
background
-
3
seperator
-
4
meltpool
-