Add full dataset card

README.md

@@ -1,5 +1,6 @@
 ---
-annotations_creators: []
+annotations_creators:
+- expert-generated
 language: en
 license: cc-by-nc-3.0
 size_categories:
@@ -8,15 +9,11 @@ task_categories:
 - image-classification
 - object-detection
 task_ids: []
-pretty_name: insplad
+pretty_name: InsPLAD
 tags:
 - anomaly-detection
 - fiftyone
 - image
-- image-classification
-- image-classification
-- object-detection
-- object-detection
 - power-line-inspection
 - uav
 description: InsPLAD is a Power Line Asset Inspection Dataset and Benchmark containing
@@ -72,13 +69,7 @@ dataset_summary: '
   '
 ---
 
-# Dataset Card for insplad
-
-<!-- Provide a quick summary of the dataset. -->
-
-
-
-
+# Dataset Card for InsPLAD
 
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 49706 samples.
 
@@ -104,135 +95,235 @@ dataset = load_from_hub("harpreetsahota/InsPLAD")
 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
+InsPLAD (Inspection of Power Line Assets Dataset) is a Power Line Asset
+Inspection Dataset and Benchmark built from 10,561 high-resolution UAV
+images of real, operating power lines. It covers the three tasks of a
+typical power line visual inspection pipeline: (1) object detection of 17
+unique power line asset categories in full UAV frames (InsPLAD-det), (2)
+supervised classification of visible defects on cropped asset images for 5
+asset categories, and (3) unsupervised anomaly detection (normal vs.
+anomalous) on cropped asset images for all 17 asset categories. This
+FiftyOne build merges all three official sub-datasets into a single
+dataset, distinguished by a `task` field and per-task saved views.
+
+- **Curated by:** André Luiz Vieira-e-Silva, Heitor Felix, Francisco
+  Simões, Veronica Teichrieb (Universidade Federal de Pernambuco / Centro
+  de Informática; Universidade Federal Rural de Pernambuco)
+- **Funded by:** Coordenação de Aperfeiçoamento de Pessoal de Nível
+  Superior (CAPES), Brazil; National Council for Scientific and
+  Technological Development (CNPq), Brazil
+- **Shared by:** The original authors, via GitHub and Mendeley Data
+- **Language(s):** en (asset/defect class names; not an NLP dataset)
 - **License:** cc-by-nc-3.0
 
-### Dataset Sources [optional]
+### Dataset Sources
 
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/andreluizbvs/InsPLAD
+- **Paper:** InsPLAD: A Dataset and Benchmark for Power Line Asset
+  Inspection in UAV Images, International Journal of Remote Sensing (2023),
+  https://arxiv.org/abs/2311.01619. A companion paper using the same data,
+  Attention Modules Improve Image-Level Anomaly Detection for Industrial
+  Inspection: A DifferNet Case Study, WACV 2024,
+  https://arxiv.org/abs/2311.02747
+- **Original data download:** https://data.mendeley.com/datasets/5n3fjgvfyz/1
 
 ## 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]
+Training and benchmarking object detectors on power line hardware
+categories; training and benchmarking supervised defect classifiers on
+cropped asset images; training and benchmarking unsupervised anomaly
+detection methods on cropped asset images; research into rare-class data
+curation and annotation-efficient workflows (the class distribution is
+heavily long-tailed, from 3,169 images down to 56 for the rarest raw
+category).
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+Any commercial use — the source license (CC BY-NC 3.0) is non-commercial
+only. The dataset is not intended or suitable for identifying individuals;
+it contains no personal or sensitive data by design (aerial images of
+power line hardware only). Asset appearance reflects a specific utility
+company's hardware standards in Brazil and may not generalize to power
+line hardware from other manufacturers, regions, or voltage classes
+without further validation.
 
 ## 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. -->
-
-[More Information Needed]
+This is a flat image dataset (`media_type = "image"`), not grouped or
+video, with **49,706 samples** total, combining InsPLAD's three official
+sub-datasets into one FiftyOne dataset via a `task` field. Three saved
+views are provided, one per sub-task: `InsPLAD-det` (10,561 samples),
+`InsPLAD-fault-supervised` (11,525 samples), `InsPLAD-anomaly` (27,620
+samples).
+
+### Fields
+
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `task` | `StringField` | Which sub-dataset this sample belongs to: `detection`, `fault_classification`, or `anomaly_detection` |
+| `ground_truth` | `Detections` | Bounding boxes over 18 raw power line asset categories (InsPLAD-det COCO annotations, converted to relative `[x, y, w, h]`); populated only for `task == "detection"` |
+| `asset` | `StringField` | Power line asset category of the cropped image (5 distinct values for `fault_classification`, 17 for `anomaly_detection`); not populated for `detection` samples |
+| `fault` | `Classification` | Supervised fault-classification label: `good`, `rust`, `missing_cap`, or `bird_nest`; populated only for `task == "fault_classification"` |
+| `anomaly` | `Classification` | Binary label: `normal` or `anomaly`; populated only for `task == "anomaly_detection"` |
+| `defect` | `StringField` | Specific defect subtype for anomaly-detection samples: `none`, `rust`, `missing_cap`, `bird_nest`, `torn`, or `peeling_paint`; populated only for `task == "anomaly_detection"` |
+| `tags` | list of `str` | Encodes both the sub-dataset (`det`, `fault`, or `anomaly`) and the original split (`train`, `val`, or `test`) |
+
+### Label types and why
+
+- `ground_truth` uses `Detections` because InsPLAD-det's source annotations
+  are 2D bounding boxes (no segmentation masks, despite the COCO schema
+  having a `segmentation` field — it is always empty in the source data).
+- `fault` and `anomaly` use `Classification` (not `Classifications`)
+  because each cropped image in these sub-datasets carries exactly one
+  label, encoded by its containing folder in the source data.
+- `defect` is stored as a plain string field, separate from the `anomaly`
+  Classification, so the specific defect subtype can be filtered/faceted
+  independently of the binary normal/anomaly label.
+
+### `dataset.info`
+
+`dataset.info` records `source` (the GitHub repository URL), `paper` (the
+arXiv URL), and `license` (`CC BY-NC 3.0`).
+
+### Parsing decisions
+
+- **Duplicate COCO entries merged.** InsPLAD-det's `instances_train.json`
+  contains 46 `file_name`s that appear under two different `image_id`s,
+  each holding a disjoint subset of that image's boxes. These were merged
+  into one sample per file with the union of both `image_id`s' detections.
+  As a result, the true unique image count for InsPLAD-det is **10,561**,
+  not the 10,607 implied by the paper's headline image count.
+- **Bounding box conversion.** Source boxes are absolute-pixel COCO
+  `[x, y, w, h]`; converted to FiftyOne's relative `[0, 1]` `[x, y, w, h]`
+  by dividing by each image's width/height.
+- **`sphere` category kept as-is.** InsPLAD-det's COCO `categories` list
+  includes an 18th category, `sphere` (26 boxes, train split only), which
+  is not one of the paper's 17 named asset categories or its per-class
+  benchmark table. It is retained in `ground_truth` rather than dropped,
+  since the source data includes it.
+- **Fault-classification label vocabulary normalized.** In the source
+  data, `supervised_fault_classification`'s `polymer-insulator-upper-shackle`
+  asset used Portuguese folder names in its `train` split (`corrosão`,
+  `normal`) but English folder names in `val` (`rust`, `good`) for the
+  same two conditions. Both were mapped to `rust`/`good` for consistency
+  with the other four fault-classification asset categories.
+- **Minor spelling normalized across sub-datasets.** The same defect
+  concept was spelled differently between the supervised and unsupervised
+  sub-datasets in the source data (`missing-cap` vs. `missingcap`;
+  `bird-nest` vs. `nest`). Both were normalized to `missing_cap` and
+  `bird_nest` respectively across both `fault` and `defect` fields.
+- **`defect` uses `"none"` for normal anomaly-detection samples**, rather
+  than leaving the field unset, for consistent filtering/faceting.
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+Automated visual inspection of power line assets is an active research
+area, but most power line inspection datasets are proprietary, undisclosed
+by the utility companies and agencies that hold them. InsPLAD was created
+to provide the first large, real-world, publicly available dataset and
+benchmark covering all three stages of a typical inspection pipeline —
+asset detection, defect classification, and anomaly detection — on the
+same underlying asset categories.
 
 ### 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. -->
-
-[More Information Needed]
+Images were captured by UAV (drone) during real-world inspections of
+operating power lines, at 1920×1080 resolution, under varied environmental
+conditions, orientations, and distances. `InsPLAD-fault`'s two sub-datasets
+(`supervised_fault_classification` and `unsupervised_anomaly_detection`)
+were derived from InsPLAD-det: annotated objects for five (supervised) and
+seventeen (anomaly detection) asset categories were cropped to a
+near-square proportion and labeled at the image level according to visible
+condition (normal, or the specific defect present). For supervised fault
+classification, faulty training samples were augmented roughly 10x using
+the Albumentations library to balance otherwise rare defect classes; for
+unsupervised anomaly detection, defective examples were deliberately
+withheld from training and included only in the test split, consistent
+with the unsupervised anomaly detection setting.
 
 #### 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. -->
-
-[More Information Needed]
+UAV inspection imagery of real, operating power lines, collected by the
+Voxar Labs group at Universidade Federal de Pernambuco.
 
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+### Annotations
 
 #### Annotation process
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
+Bounding boxes for InsPLAD-det were drawn using the LabelImg tool by two
+annotators, instructed by specialists on asset naming conventions and the
+regions of interest that should be contained in each box, with annotators
+in constant contact to standardize labeling. Fault and anomaly labels were
+applied at the image level based on the visible condition of each cropped
+asset.
 
 #### Who are the annotators?
 
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
+Two human annotators, instructed and guided by domain specialists, per the
+source paper.
 
 #### 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. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+Not addressed explicitly in the source paper. Images are aerial captures
+of power line hardware and surrounding infrastructure; there is no
+statement in the source material regarding incidental capture of people or
+other personal data. `[More Information Needed]`
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```
+@article{doi:10.1080/01431161.2023.2283900,
+   author    = {André Luiz Buarque Vieira e Silva, Heitor de Castro Felix, Franscisco Paulo Magalhães Simões, Veronica Teichrieb, Michel dos Santos, Hemir Santiago, Virginia Sgotti and Henrique Lott Neto},
+   title     = {InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images},
+   journal   = {International Journal of Remote Sensing},
+   volume    = {44},
+   number    = {23},
+   pages     = {1-27},
+   year      = {2023},
+   publisher = {Taylor & Francis},
+   doi       = {10.1080/01431161.2023.2283900},
+   URL       = {https://doi.org/10.1080/01431161.2023.2283900},
+   eprint    = {https://doi.org/10.1080/01431161.2023.2283900},
+}
+
+@InProceedings{Vieira_2024_WACV,
+    author    = {e Silva, Andr\'e Luiz Vieira and Sim\~oes, Francisco and Kowerko, Danny and Schlosser, Tobias and Battisti, Felipe and Teichrieb, Veronica},
+    title     = {Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study},
+    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
+    month     = {January},
+    year      = {2024},
+    pages     = {8246-8255}
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
+Vieira-e-Silva, A. L. B., de Castro Felix, H., Simões, F. P. M., Teichrieb, V., dos Santos, M., Santiago, H., Sgotti, V., & Lott Neto, H. (2023). InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images. *International Journal of Remote Sensing*, 44(23), 1-27.
 
-## More Information [optional]
+## More Information
 
-[More Information Needed]
+This FiftyOne build merges the three official InsPLAD sub-datasets
+(`InsPLAD-det.zip`, `supervised_fault_classification.zip`,
+`unsupervised_anomaly_detection.zip`) into one dataset. See the source
+repository (https://github.com/andreluizbvs/InsPLAD) for the original,
+separately-distributed sub-datasets and additional documentation.
 
-## Dataset Card Authors [optional]
+## Dataset Card Authors
 
-[More Information Needed]
+Harpreet Sahota (FiftyOne / Voxel51 conversion and card)
 
 ## Dataset Card Contact
 
-[More Information Needed]
+harpreetsahota