Datasets:

Voxel51
/

Kolektor_Surface_Defect

@@ -46,7 +46,7 @@ dataset_summary: '
   # Note: other available arguments include ''max_samples'', etc
-  dataset = load_from_hub("harpreetsahota/Kolektor_Surface_Defect")
   # Launch the App
@@ -60,10 +60,13 @@ dataset_summary: '
 # Dataset Card for kolektorsdd
-<!-- Provide a quick summary of the dataset. -->
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 399 samples.
@@ -84,141 +87,128 @@ from fiftyone.utils.huggingface import load_from_hub
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("harpreetsahota/Kolektor_Surface_Defect")
 # Launch the App
 session = fo.launch_app(dataset)
 ```
-## Dataset Details
-### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the dataset is intended to be used. -->
-### Direct Use
-<!-- This section describes suitable use cases for the dataset. -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
-## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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-## Dataset Creation
-### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
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-### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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-#### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
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-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
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-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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 **APA:**
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-## Glossary [optional]
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   # Note: other available arguments include ''max_samples'', etc
+  dataset = load_from_hub("Voxel51/Kolektor_Surface_Defect")
   # Launch the App
 # Dataset Card for kolektorsdd
+KolektorSDD (Kolektor Surface-Defect Dataset) is a grayscale industrial surface-inspection
+dataset of electrical commutators.
+This FiftyOne dataset uses the **box-annotation release** intended for the ICPR 2021 and
+COMIND 2021 papers ([download](https://go.vicos.si/kolektorsddboxes)): one sample per
+surface image, with defect regions annotated as axis-aligned bounding boxes stored as filled
+rectangles in the label masks.
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 399 samples.
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
+dataset = load_from_hub("Voxel51/Kolektor_Surface_Defect")
 # Launch the App
 session = fo.launch_app(dataset)
 ```
+## Dataset Details
+- **Curated by:** Domen Tabernik, Samo Šela, Jure Skvarč, Danijel Skočaj (University of Ljubljana / ViCoS Lab); images provided and annotated by Kolektor Group d.o.o.
+- **Paper (dataset):** [Segmentation-Based Deep-Learning Approach for Surface-Defect Detection](https://doi.org/10.1007/s10845-019-01476-x)
+- **Box annotations used in:** [End-to-end training of a two-stage neural network for defect detection](https://arxiv.org/abs/2007.07676) (ICPR 2020) and [Mixed supervision for surface-defect detection](http://prints.vicos.si/publications/385) (Computers in Industry, 2021)
+- **Project page:** https://www.vicos.si/resources/kolektorsdd/
+- **Download (this release):** https://go.vicos.si/kolektorsddboxes
+- **License:** [CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/) (non-commercial; contact [Danijel Skočaj](https://www.vicos.si/people/danijel_skocaj/) for commercial use)
+### What the data contains
+Images were captured in a controlled industrial environment. Each sample is one
+non-overlapping view of a commutator surface. Defects are microscopic fractures or
+cracks in the plastic embedding.
+| Property | Value |
+|---|---|
+| Total images | 399 |
+| Physical items (boards) | 50 (`kos01`–`kos50`) |
+| Surfaces per item | 8 (`Part0`–`Part7`) |
+| Defective images | 52 |
+| Non-defective images | 347 |
+| Image type | Grayscale JPG |
+| Original size | 500 px wide × 1240–1270 px tall |
+| Recommended eval size | 512 × 1408 px (per dataset authors) |
+Defect visibility: for 48 items the defect appears in exactly one image; for 2 items
+it appears in two images.
+A separate **fine pixel-annotation release** exists for the JIM2019 paper
+([download](https://go.vicos.si/kolektorsdd)). That version is not what this card describes.
+### Raw download layout
+```
+kolektorsdd/
+  kos01/
+    Part0.jpg
+    Part0_label.bmp
+    Part1.jpg
+    Part1_label.bmp
+    ...
+  kos02/
+    ...
+```
+- `Part*.jpg` — surface image
+- `Part*_label.bmp` — defect annotation mask (non-zero = defect region)
+In this box-annotation release, each defective mask is a **filled axis-aligned bounding box**
+around the defect, not a precise pixel-wise segmentation of the crack shape.
+### Train/test splits
+The authors evaluate with **3-fold cross-validation**, keeping all 8 images of the
+same physical item in the same fold. Official split files:
+[KolektorSDD-training-splits.zip](https://data.vicos.si/datasets/KSDD/KolektorSDD-training-splits.zip).
+This FiftyOne dataset does **not** assign fold/split labels. Add them externally if needed.
+---
+## FiftyOne Dataset Structure
+| Property | Value |
+|---|---|
+| Hub dataset | `harpreetsahota/Kolektor_Surface_Defect` |
+| Local dataset name | `kolektorsdd` |
+| Media type | `image` |
+| Samples | 399 |
+### Sample fields
+| Field | Type | Description |
+|---|---|---|
+| `filepath` | `StringField` | Path to source `Part*.jpg` |
+| `board_id` | `StringField` | Board directory name, e.g. `"kos01"` |
+| `has_defect` | `BooleanField` | `True` if the mask contains any foreground pixel |
+| `ground_truth` | `EmbeddedDocumentField(Segmentation)` | Binarized mask (`0` = background, `1` = defect) |
+The local parser (`parse_to_fo.py`) reads each BMP label and stores a `{0, 1}` mask on
+the sample. For defective images in this release, the foreground region is a filled
+bounding box rather than a tight defect outline.
+---
+## Citation
+**BibTeX (dataset):**
+```bibtex
+@article{Tabernik2019JIM,
+  author  = {Tabernik, Domen and {\v{S}}ela, Samo and Skvar{\v{c}}, Jure and Sko{\v{c}}aj, Danijel},
+  journal = {Journal of Intelligent Manufacturing},
+  title   = {{Segmentation-Based Deep-Learning Approach for Surface-Defect Detection}},
+  year    = {2019},
+  month   = {May},
+  day     = {15},
+  issn    = {1572-8145},
+  doi     = {10.1007/s10845-019-01476-x}
+}
+```
+**BibTeX (box annotations / mixed supervision):**
+```bibtex
+@article{Bozic2021COMIND,
+  author  = {Bo{\v{z}}i{\v{c}}, Jakob and Tabernik, Domen and Sko{\v{c}}aj, Danijel},
+  journal = {Computers in Industry},
+  title   = {{Mixed supervision for surface-defect detection: from weakly to fully supervised learning}},
+  year    = {2021}
+}
+```
 **APA:**
+Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2019). Segmentation-Based Deep-Learning Approach for Surface-Defect Detection. *Journal of Intelligent Manufacturing*. https://doi.org/10.1007/s10845-019-01476-x