README.md

metadata

license: cc-by-4.0
task_categories:
  - visual-question-answering
  - image-classification
language:
  - en
tags:
  - additive-manufacturing
  - laser-powder-bed-fusion
  - meltpool
  - near-infrared
  - thermal-monitoring
  - vqa
pretty_name: NIR Meltpool Thermal-State VQA
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: validation
        path: data/validation-*.parquet
      - split: test
        path: data/test-*.parquet
dataset_info:
  features:
    - name: query
      dtype: string
    - name: image
      dtype: image
    - name: annot
      dtype: string
    - name: reasoning
      dtype: string
    - name: cate
      dtype: string
    - name: task
      dtype: string
    - name: metadata
      struct:
        - name: sample
          dtype: int32
        - name: source_tif
          dtype: string
        - name: label_id
          dtype: int32
        - name: label_name
          dtype: string
        - name: time
          dtype: int64
        - name: position_X
          dtype: float64
        - name: position_Z
          dtype: float64
        - name: V
          dtype: float64
        - name: image_height
          dtype: int32
        - name: image_width
          dtype: int32
        - name: mean_intensity_8bit
          dtype: float64
        - name: bright_core_fraction
          dtype: float64
        - name: norm_low_uint16
          dtype: float64
        - name: norm_high_uint16
          dtype: float64
        - name: split
          dtype: string

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	overheat
2	underheat	5	strong overheat

Classes present in this release: only sample 588 images were available, so only baseline, edge, strong underheat appear. The query still lists all six options and the schema supports all six for when the remaining samples are added.

Record schema

Each record has the fields:

• query — the question (fixed, lists all six options).
• image — the NIR frame (embedded PNG; HF Image feature).
• annot — ground-truth class name (the answer), from the source CSV label.
• reasoning — a grounded chain-of-thought referencing the frame's NIR intensity.
• cate — meltpool_thermal_state.
• task — meltpool_thermal_state_classification.
• metadata — provenance & process signals: source TIF name, label_id, stage position (position_X, position_Z), V, capture time, per-image intensity stats, and the global normalization constants.

Splits

Total: 77,280 records.

split	count	per class
train	61,825	baseline: 39,654, edge: 13,051, strong underheat: 9,120
validation	7,728	baseline: 4,957, edge: 1,631, strong underheat: 1,140
test	7,727	baseline: 4,956, edge: 1,631, strong underheat: 1,140

Split strategy — leakage-aware. Frames are ~2 us apart and adjacent frames are near-duplicates. Splitting is time-block stratified: within each label the frames are ordered by capture time and cut into 10 contiguous blocks (8 train / 1 validation / 1 test), so near-duplicate neighbours stay in the same split.

Image processing

Source TIFs are single-channel uint16. They are mapped to 8-bit RGB PNG with a single global linear normalization (clip to the dataset-wide 1st/99th percentile, [norm_low_uint16, norm_high_uint16], then scale to 0-255) and channel-replicated. Global (not per-image) normalization is deliberate: absolute brightness is the physical cue that distinguishes under- vs over-heated pools, and per-image contrast stretching would destroy it.

Usage

from datasets import load_dataset
ds = load_dataset("YOUR_USERNAME/YOUR_REPO")
ex = ds["train"][0]
ex["image"]      # PIL.Image
ex["query"], ex["annot"], ex["reasoning"]
ex["metadata"]["label_id"]

Provenance & license

Converted from hyperspectral_nir_meltpool_dataset.csv + hyperspectral_nir_meltpool_images_2.zip (sample 588). Ground-truth labels come directly from the CSV label column, matched to images by exact filename (verified 1:1 for all 77,280 frames). Released under CC-BY-4.0; if the upstream meltpool dataset carries a different license, update this field before publishing.