---
pretty_name: KSDD2 (Kolektor Surface-Defect Dataset 2) — manual-download loader
license: cc-by-nc-sa-4.0
tags:
  - computer-vision
  - image-classification
  - anomaly-detection
  - industrial-inspection
  - image-segmentation
task_categories:
  - image-classification
  - image-segmentation
configs:
  - config_name: image_only
    description: "Flat train/test folders with images only (no labels)."
  - config_name: classification_from_list
    description: "Flat train/test + defect_list.txt (or .csv) ⇒ labels."
  - config_name: classification_from_pyb
    description: "Flat train/test + split_weakly_*.pyb ⇒ labels (auto union)."
  - config_name: classification
    description: "ok/ and defect/ subfolders ⇒ labels."
  - config_name: with_mask
    description: "classification + optional mask_path matched by name from masks folders."
---

# 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.

---

## 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**:

```bash
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.

---

## 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).

---

## 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
```

---

## Quickstart

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

### 1) Flat, images only

```python
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

```python
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:

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

### 4) Labeled subfolders

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

### 5) Labeled subfolders + masks

```python
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.

---

## 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.

---

## 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.