Joshyxwa/data_draft

TechJam 2026 Data Draft

Dataset Summary

This private research draft validates a binary image-classifier pipeline: the local data loader, feature extractor, classifier head, calibration step, and robustness evaluation. It is not a public benchmark, a claim of real-world detection quality, or part of the production 80k-master corpus.

The package has 10,000 canonical RGB PNG images. Every image has a portable manifest record with a label, split, source provenance, hashes, image properties, and processing history.

Composition

Source	Real	Fully AI	Total	Notes
SID-Set	2,500	2,500	5,000	Canonical local snapshot; source-provided real/synthetic label
WildFake	2,500	2,500	5,000	Deterministic subset from complete local source archives
Total	5,000	5,000	10,000	Exact class and source balance

WildFake real examples are evenly selected from afhq, celebahq, church, ffhq, and imagenet. Fully-AI examples are evenly selected from adm, ddim, ddpm, imagen, and gan_based. Each WildFake family contributes 500 images. COCO and DALL-E families are excluded.

Splits

Split	Real	Fully AI	Total	Intended use
train	3,500	3,500	7,000	Fit an architecture or classifier head
dev	750	750	1,500	Select settings and inspect errors
calibration	750	750	1,500	Set thresholds or fit probability calibration only

Do not move records between splits or train on dev/calibration images or their derivatives. The builder rejects cross-split collisions by asset ID, base ID, lineage ID, exact SHA-256, exact perceptual hash, and near perceptual hash distance at most five during WildFake selection.

Labels

Value	Meaning	Source evidence
real	A source-labelled non-synthetic photograph/image	SID source path role or WildFake real archive role
ai_full	A source-labelled, fully generated AI image	SID source path role or WildFake generator archive role

Labels are source-provided, not manually re-annotated. label_evidence and label_confidence are present for every row; the confidence is 0.8 for this draft. There are no partial_ai, recaptured_ai, or edited-image labels.

Files And Metadata

File	Purpose
images/<split>/<label>/<asset_id>.png	Canonical RGB image files
manifest.parquet	Full provenance, integrity, quality, and image-property manifest
metadata.jsonl	Compact image/label/split/source index for simple loaders
verification.json	Completed local decode, hash, and split-leakage audit

manifest.parquet includes these field groups:

Group	Key fields
Identity and split	asset_id, base_id, lineage_id, split, label
Label traceability	label_evidence, label_confidence, real_subtype, ai_subtype
Source provenance	source_dataset, source_family, source_uri, source_archive, archive_member
Image integrity	sha256, phash, width, height, aspect_ratio, file_format, file_size_bytes
Processing and quality	processing_track, processing_history_status, provenance_status, quality_status
Rights policy	licence, licence_name, allowed_for_training, allowed_for_public_demo

Paths in the manifest are relative to this dataset repository. After download:

from pathlib import Path

import pandas as pd
from PIL import Image

root = Path("path/to/data_draft")
manifest = pd.read_parquet(root / "manifest.parquet")
row = manifest.iloc[0]
image = Image.open(root / row.path).convert("RGB")
label = row.label  # "real" or "ai_full"

Quality Checks

The packaged manifest passed the following local checks:

• 10,000 of 10,000 images decode as canonical RGB files.
• Recorded SHA-256, perceptual hash, width, and height match every image file.
• No detected cross-split collision by base ID, lineage ID, parent ID, exact hash, or exact perceptual hash.
• Final class counts are exactly balanced in every split.
• The package contains no raw source archives, API keys, model checkpoints, production generations, COCO records, or DALL-E records.

Provenance And Restrictions

SID-Set rows retain upstream source URI and CC-BY-4.0 metadata. WildFake rows retain the exact ZIP archive and member path but are marked licence_audit_required. Therefore:

• Keep this repository private.
• Do not redistribute WildFake-derived files or make public demos from them until the upstream licence review is complete.
• Do not add these records to the production 80k corpus or use them for benchmark/generalization claims.

The other licence tag describes the mixed, restricted state of the package; it grants no rights beyond the underlying source datasets.

Limitations

This small mixture is useful for pipeline and architecture smoke tests, but it is source-family constrained. Its results may be optimistic because training, development, and calibration all originate from the same two upstream datasets. It does not model social-media reposting, video-frame extraction, partial edits, recapture, contemporary held-out generators, or real-world class prevalence. Report per-source and per-transform errors, not only aggregate AUROC/AUPRC.

Reproducibility

The deterministic selection seed is 20260830. manifest.parquet preserves the selection and processing lineage, stable asset/base/lineage identifiers, and content hashes required to audit a local rebuild before training.