KSDD2 (manual-download loader)

This repo provides a manual-download loading script for Kolektor Surface-Defect Dataset 2 (KSDD2). No images are hosted here. Users must download KSDD2 from the official page and load locally via load_dataset(..., data_dir=...).

• Official page (license & download): https://www.vicos.si/resources/kolektorsdd2/
• Dataset license: CC BY-NC-SA 4.0 (non‑commercial; attribution; share‑alike). For commercial usage, follow the authors’ instructions on the official page.

What this loader does: read your local KSDD2 folder, return a standard DatasetDict with images and metadata, without moving or copying files.

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Recommended environment (important)

This dataset card uses a custom loading script (trust_remote_code=True). Newer versions of datasets (v4.x) do not execute loading scripts from the Hub.
To load this dataset from the Hub script, please install the tested versions:

pip install "datasets==3.2.0" "huggingface_hub<0.27"

If you must use newer releases, consider the built‑in imagefolder loader (labels/masks & pyb auto‑labeling will not be available), or run a local helper to produce Arrow/Parquet then load_from_disk. For best UX, we recommend the tested versions above.

Windows note: you may see a harmless warning about symlinks from huggingface_hub. It can be ignored, or disable via HF_HUB_DISABLE_SYMLINK_WARNING=1, or enable Windows Developer Mode / run as admin.

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Features by config

Config	Features	When to use
image_only	{"image", "path"}	You have flat train/ and test/ (images only), no labels yet.
classification_from_list	{"image", "label", "path"}	Flat folders + you provide defect_list.txt (one filename per line).
classification_from_pyb	{"image", "label", "path"}	Flat folders + you have split_weakly_*.pyb files (labels auto‑derived).
classification	{"image", "label", "path"}	Your data is already split into ok/ and defect/ subfolders.
with_mask	{"image", "label", "path", "mask_path"}	Same as classification, and you also have a masks folder with same names.

label is a ClassLabel with ["good", "defect"].
mask_path is a string (empty if not found).

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Folder layouts (examples)

A) Flat layout (no subfolders under split)

KSDD2/
  train/*.png|jpg
  test/*.png|jpg
  split_weakly_0.pyb
  split_weakly_16.pyb
  ...

B) Labeled subfolders

KSDD2/
  train/
    ok/*.png|jpg
    defect/*.png|jpg
    [masks | masks_defect | ground_truth | gt | label | labels]/*.png   # optional, for with_mask
  test/
    ok/*.png|jpg
    defect/*.png|jpg
    [masks | masks_defect | ground_truth | gt | label | labels]/*.png

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Quickstart

All snippets below assume the tested versions mentioned above and trust_remote_code=True.

1) Flat, images only

from datasets import load_dataset

ds = load_dataset("OliverOnHF/ksdd2",
                  name="image_only",
                  data_dir=r"<Your Local KSDD2 dataset path>",
                  trust_remote_code=True)
print(ds)
print(ds["train"][0])  # {"image": ..., "path": "..."}

2) Flat + auto labels from pyb

ds = load_dataset("OliverOnHF/ksdd2",
                  name="classification_from_pyb",
                  data_dir=r"<Your Local KSDD2 dataset path>",
                  trust_remote_code=True)
print(ds["train"].features)  # ClassLabel(names=['good','defect'])
print(ds["train"][0])        # {"image": ..., "label": 0/1, "path": "..."}

How it works: the loader scans all split_weakly_*.pyb next to your train/ and test/, extracts filename strings and/or numeric IDs, matches them to your image basenames (e.g. 10023 → 10023.png), and takes the union across all pyb files: if a name appears in any pyb, it is labeled as defect.

3) Flat + your defect list

Place a defect_list.txt (or .csv) inside each split:

KSDD2/
  train/
    defect_list.txt    # one filename per line; comments (#) and blanks ignored
  test/
    defect_list.txt

Then:

ds = load_dataset("OliverOnHF/ksdd2",
                  name="classification_from_list",
                  data_dir=r"<Your Local KSDD2 dataset path>",
                  trust_remote_code=True)

4) Labeled subfolders

ds = load_dataset("OliverOnHF/ksdd2",
                  name="classification",
                  data_dir=r"<Your Local KSDD2 dataset path>",
                  trust_remote_code=True)

5) Labeled subfolders + masks

ds = load_dataset("OliverOnHF/ksdd2",
                  name="with_mask",
                  data_dir=r"<Your Local KSDD2 dataset path>",
                  trust_remote_code=True)

The loader looks up masks by same filename under any of: masks, masks_defect, ground_truth, gt, label, labels. If not found, mask_path is an empty string.

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Troubleshooting

• “Dataset scripts are no longer supported” or cannot trust_remote_code: use the tested versions shown above (datasets==3.2.0, huggingface_hub<0.27).
• “Cannot find class folders … expect ok and defect”: you selected classification/with_mask but your layout is flat. Use classification_from_pyb / classification_from_list, or reorganize into ok/ and defect/.
• No labels produced in classification_from_pyb: make sure split_weakly_*.pyb sits next to train/ and test/, and that image basenames contain numeric IDs or exact names referenced by the pyb files.
• Windows symlink warning from huggingface_hub: harmless; can be ignored.

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License

• Dataset (KSDD2): CC BY-NC-SA 4.0 — see the official KSDD2 page. This repo does not redistribute any images.
• Loader code in this repo: MIT (see LICENSE).

Citation

Please cite KSDD2 as requested by the authors on the official page.