README.md

---
pretty_name: DD1 Cropped OT VQA Classification
tags:
  - visual-question-answering
  - image-classification
  - industrial
  - additive-manufacturing
  - sft
task_categories:
  - visual-question-answering
  - image-classification
---

# DD1 Cropped OT VQA Classification

Answer-only VQA/SFT binary classification data derived from
`Croped Defects/OT`. This is a patch-classification dataset, not an
object-detection dataset. Each record asks the model to answer exactly `good`
or `defect`.

## Source and construction

- Source: `/Users/duoduo/Downloads/research/DD1-Annotated Image Dataset for defects detection in Laser Powder Bed Fusion/Croped Defects/OT`
- Source images: 1550 Good full frames and
  1124 Defects crops
- Original Good geometry: 2000x2000
- Retained Defects crops: 1061
- Excluded Defects crops: 63
- Paired Good crops generated: 1061
- Final records: 2122

Defects crops with either side below 32 pixels
are excluded. Their source path, dimensions, reason, split and split group are
stored in `excluded_defects.jsonl`.

For every retained Defects crop, the builder creates one Good crop with exactly
the same width and height. Candidate Good sources are restricted to the same
build and split. It evaluates
12 deterministic random crops and
selects the candidate whose coarse grayscale mean and standard deviation most
closely match the Defects crop.

Both labels are converted to RGB and re-encoded using the same Pillow JPEG
pipeline: quality 95, 4:4:4 subsampling,
non-progressive. This removes source channel/encoder configuration as a direct
label cue, although JPEG byte size may still reflect genuine visual complexity.

Thirty fixed English query templates are assigned deterministically from the
derived image SHA-256. The complete list is in `query_templates.json`.

## Schema

| field | type | meaning |
|---|---|---|
| `query` | string | one of 30 deterministic classification prompts |
| `image` | Image | JPEG bytes embedded in Parquet |
| `annot` | string | exact gold answer: `good` or `defect` |
| `reasoning` | null | answer-only dataset |
| `cate` | string | `C` |
| `task` | string | `T-C1` |
| `metadata` | string | JSON provenance, geometry, crop, hash and split |

`annot` is the direct SFT answer and `reasoning` is null on every row.

## Split counts

| split | good | defect | total |
|---|---:|---:|---:|
| train | 819 | 819 | 1638 |
| validation | 131 | 131 | 262 |
| test | 111 | 111 | 222 |

Adjacent layer IDs are grouped into blocks of
20 before deterministic split hashing.
All images derived from a split group remain in one split. Validation confirms
that no split group appears in multiple splits.

## Shortcut control

- Good and Defect labels are balanced in every split.
- Every pair has identical width, height, area and aspect ratio.
- Good crops use the same build and split as their geometry template.
- Both labels use the same RGB/JPEG encoding pipeline.
- Exact derived-image duplicates are rejected.
- Source hashes, derived hashes, crop coordinates and pair IDs are retained.

These controls remove the original full-frame-versus-crop size shortcut. They
do not prove that every background or border correlation is causal. An OT
nuisance-feature audit found 50.0% test accuracy from geometry alone, 85.1%
from outer-border statistics and 92.8% from global brightness/texture
statistics. The latter measurements can contain genuine defect signal, but
they also flag residual sensitivity to crop composition. For robust deployment,
evaluate random crop translation, context expansion, padding and defect
occlusion.

## Training input contract

Use only `query` and decoded image pixels as model input. Use `annot` as the
target.

Do not serialize `image.path` or `metadata` into the prompt. The embedded image
path and provenance metadata intentionally contain source/condition information
for auditing and can reveal the label to a text model. If a training framework
automatically exposes paths, replace them with neutral hash-based names first.

## Metadata

`metadata` is a JSON string containing:

- dataset identity: `schema_version`, `family`, `representation`, `modality`
- label/provenance: `condition`, `source_file`, `derivation`,
  `geometry_template`
- geometry: `image_width`, `image_height`, `aspect_ratio`,
  `crop_xywh_in_good_source`
- grouping: `build_id`, `layer_id`, `split`, `split_group`, `pair_id`
- integrity: `source_sha256`, `image_sha256`
- generation: `query_variant`, `jpeg_reencoded`, `jpeg_quality`,
  `jpeg_subsampling`, `appearance_match_distance`, `appearance_candidates`
- policy: `reasoning_policy`, `label_scope`

## Files

- `data/train-00000-of-00001.parquet`
- `data/validation-00000-of-00001.parquet`
- `data/test-00000-of-00001.parquet`
- `build_dd1_cropped_ot_vqa.py`: reproducible builder
- `build_report.json`: parameters, counts, SHA-256 values and validation result
- `excluded_defects.jsonl`: rejected source crops and rejection reasons
- `query_templates.json`: all 30 deterministic prompts
- `requirements.txt`: pinned build dependencies
- `README.md`: this document

## Load

```python
from datasets import load_dataset

ds = load_dataset(
    "parquet",
    data_files={
        "train": "data/train-00000-of-00001.parquet",
        "validation": "data/validation-00000-of-00001.parquet",
        "test": "data/test-00000-of-00001.parquet",
    },
)
```

Example SFT mapping:

```python
record = ds["train"][0]
model_input = {
    "query": record["query"],
    "image": record["image"],
}
target = record["annot"]
```

## Reproduce

```bash
python3 -m pip install -r requirements.txt
python3 build_dd1_cropped_ot_vqa.py \
  --source /path/to/Croped\ Defects/OT \
  --output /path/to/DD1_OT_VQA_classification
```

The build is deterministic and refuses to overwrite an existing output.

## Validation

The builder and an independent post-build check verify:

- exact seven-column order
- embedded, decodable JPEG bytes
- RGB output and stored dimensions
- balanced labels in every split
- complete Good/Defect pairs with identical geometry and split
- `annot`/`metadata.condition` consistency
- `cate=C`, `task=T-C1`, `reasoning=null`
- unique derived-image SHA-256 values
- no split-group overlap
- Parquet SHA-256 values recorded in `build_report.json`

## Limitations

- `good` and `defect` inherit the source folder labels; `good` does not prove
  absence of every possible manufacturing anomaly.
- Defects and Good images still originate from different crop-generation
  histories, so border/background correlations may remain.
- A Good full frame may supply more than one crop, always within one split.
- Crops below the legibility threshold are intentionally omitted.
- This dataset supports binary classification; it does not provide a
  localization answer.