Add full dataset card

README.md

@@ -1,5 +1,6 @@
 ---
-annotations_creators: []
+annotations_creators:
+- no-annotation
 language: en
 license: cc-by-nc-3.0
 size_categories:
@@ -7,28 +8,26 @@ size_categories:
 task_categories:
 - object-detection
 task_ids: []
-pretty_name: insplad-workshop-pool
+pretty_name: InsPLAD Workshop Pool
 tags:
 - active-learning
 - data-curation
 - fiftyone
 - image
-- image
-- object-detection
 - object-detection
 - power-line-inspection
 - uav
 - unlabeled
 description: 'A 1,754-image, media-only sample of InsPLAD-det (UAV power line inspection
   imagery), built for a hands-on FiftyOne workshop on a complete annotation workflow.
-  No label fields are attached by design; this is a genuine cold-start pool for practicing
-  compression, embedding, seeded similarity search, and annotation prioritization
-  before ever touching a model. A deterministic, balanced stratified sample (seed=51)
-  drawn from the full 10,561-image InsPLAD-det: roughly 217 images each of `tower
-  id plate`, `polymer insulator`, `glass insulator`, and `yoke` (deliberately capped
-  at the same quota so no class dominates the annotation budget), 574 images from
-  14 intact drone-flight sequences (a real near-duplicate wall), and 215 long-tail
-  images for texture.'
+  No label fields are attached by design; this is a genuine cold-start pool for
+  practicing compression, embedding, seeded similarity search, and annotation
+  prioritization before ever touching a model. A deterministic, balanced stratified
+  sample (seed=51) drawn from the full 10,561-image InsPLAD-det: roughly 217 images
+  each of `tower id plate`, `polymer insulator`, `glass insulator`, and `yoke`
+  (deliberately capped at the same quota so no class dominates the annotation
+  budget), 574 images from 14 intact drone-flight sequences (a real near-duplicate
+  wall), and 215 long-tail images for texture.'
 dataset_summary: '
 
 
@@ -76,13 +75,7 @@ dataset_summary: '
   '
 ---
 
-# Dataset Card for insplad-workshop-pool
-
-<!-- Provide a quick summary of the dataset. -->
-
-
-
-
+# Dataset Card for InsPLAD Workshop Pool
 
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 1754 samples.
 
@@ -108,135 +101,370 @@ dataset = load_from_hub("harpreetsahota/InsPLAD-workshop-pool")
 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:** cc-by-nc-3.0
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+InsPLAD Workshop Pool is a 1,754-image, **media-only** sample of
+[InsPLAD-det](https://huggingface.co/datasets/harpreetsahota/InsPLAD), built to
+teach a complete annotation workflow in FiftyOne: compress a raw image pool,
+embed it, search for examples of each target class from a handful of seed
+examples, prioritize the rest for annotation, fine-tune a detector, and correct
+its mistakes. This dataset ships with **zero label fields by design**. The point
+of the exercise is deciding which images deserve human attention before any
+labels exist. The images sampled into this pool were deliberately stratified
+(not randomly subsampled) so that every step of that workflow has something real
+to work with: genuine near-duplicate sequences, and four target classes
+deliberately balanced to roughly the same size instead of reproducing the class
+imbalance already present in the raw source data. See
+[Curation Rationale](#curation-rationale) below for exactly how, and the
+[workshop's companion GitHub repository](https://github.com/harpreetsahota204/annotation_workshop/tree/main)
+for the full, runnable pipeline and workshop materials.
+
+- **Curated by:** Harpreet Sahota (Voxel51), sampled from InsPLAD (see Dataset
+  Sources for the original dataset's curators)
+- **Funded by:** N/A (derivative sample; see original InsPLAD for its funding)
+- **Shared by:** Harpreet Sahota, via Hugging Face Hub
+- **Language(s):** en (asset class names in the source data; not an NLP dataset)
+- **License:** cc-by-nc-3.0 (inherited from InsPLAD; non-commercial use only)
+
+### Dataset Sources
+
+- **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
+- **Original data download:** https://data.mendeley.com/datasets/5n3fjgvfyz/1
+- **Full FiftyOne build (all 3 InsPLAD sub-datasets, with labels):**
+  https://huggingface.co/datasets/harpreetsahota/InsPLAD
 
 ## 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]
+Practicing (or teaching) a complete annotation loop end to end: near-duplicate
+compression, embedding-based visual exploration, seeded similarity search for a
+target class, uniqueness/representativeness-based annotation prioritization,
+detector fine-tuning on the curated subset, and model-assisted correction. Also
+useful as a small, realistic stand-in for InsPLAD-det when testing FiftyOne
+workflows without downloading the full 10,561-image, 4.2 GB source dataset.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+Not intended as a benchmark dataset for reporting detection accuracy. It is a
+deliberately non-random, stratified sample built for a specific teaching workflow,
+not an i.i.d. sample of InsPLAD-det. Any commercial use is out of scope; the
+source license (CC BY-NC 3.0) is non-commercial only. Not suitable for identifying
+individuals; it contains no personal or sensitive data by design (aerial images of
+power line hardware only).
 
 ## 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]
-
-## Dataset Creation
-
-### Curation Rationale
-
-<!-- Motivation for the creation of this dataset. -->
+This is a flat image dataset (`media_type = "image"`), not grouped or video, with
+**1,754 samples** and no splits or saved views. Every sample carries only
+FiftyOne's default fields; there is no `ground_truth`, no per-sample split tag,
+and no per-sample sampling-tier label. This is intentional: the dataset is meant
+to be loaded and explored exactly as if no prior work had been done on it.
 
-[More Information Needed]
+### Fields
 
-### Source Data
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `filepath` | `StringField` | Path to the image file |
+| `tags` | list of `str` | Empty for every sample; no split or tier tags are shipped |
+| `metadata` | `ImageMetadata` | Not populated (`None`) until `dataset.compute_metadata()` is run |
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+### `dataset.info`
 
-#### Data Collection and Processing
+```python
+{
+    "source": "https://github.com/andreluizbvs/InsPLAD",
+    "note": (
+        "Media-only workshop pool sampled from InsPLAD-det. No labels "
+        "attached by design; see 02_build_workshop_pool.py for the "
+        "sampling manifest and heldout_ground_truth.json for the real "
+        "boxes, held out until the 'close the loop' act."
+    ),
+}
+```
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+### Parsing decisions
+
+- **Media-only import, on purpose.** The staging step that builds this pool does
+  compute real bounding boxes for every sampled image (converted from InsPLAD's
+  COCO format to FiftyOne's relative `[x, y, w, h]`), but those boxes are written
+  to a separate `heldout_ground_truth.json` file and never attached to the
+  FiftyOne dataset. This dataset is the "before" half of a before/after teaching
+  exercise.
+- **No tier or split metadata shipped.** Which sampling tier (eval holdout,
+  balanced target, duplicate-wall, long-tail) or original InsPLAD split
+  (`train`/`val`) each image came from is recorded in
+  `workshop_pool_manifest.json` at build time, not carried into this Hub
+  dataset's fields, including the `eval_holdout` tag itself. That keeps the
+  pool looking like a genuine unlabeled pool, not a labeled one with fields
+  hidden. If you build the pool locally via the companion
+  `01_download_insplad_det.py` through `04_import_workshop_dataset.py`
+  scripts instead of loading from the Hub, step 4 re-applies the
+  `eval_holdout` tag to the same 100 stratified samples on import.
+- **Whole scenes only, no crops.** Unlike the full InsPLAD-fault sub-datasets
+  (cropped, near-square asset images), every image in this pool is a full UAV
+  scene from InsPLAD-det. A bounding-box task only makes sense on full scenes,
+  and every act of the target workflow (including fine-tuning and correcting a
+  detector) depends on that.
 
-[More Information Needed]
+## Dataset Creation
 
-#### Who are the source data producers?
+### Curation Rationale
 
-<!-- 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. -->
+A naive random subsample of InsPLAD-det's 10,561 images breaks the workflow this
+dataset is meant to teach: dedupe before subsampling and there's no duplicate wall
+left for the "compress" step to find; subsample without correcting for class
+frequency and the resulting pool just reproduces whatever imbalance already
+exists in the raw data (`tower id plate` has only 242 images total; some other
+classes have thousands). Instead, this pool uses a **deterministic, balanced
+stratified sample** (seed=`51`, same result every run) that guarantees every step
+of the workflow has something real to work with, at a fraction of the source
+data's size, with all 4 target classes capped at the same rough quota.
 
-[More Information Needed]
+Two scripts build this pool from the original InsPLAD-det source; both are
+included verbatim below for full reproducibility.
 
-### Annotations [optional]
+#### Step 1: Download InsPLAD-det from source
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+InsPLAD ships as a single Mendeley Data record containing three inner zips
+(`InsPLAD-det.zip`, `supervised_fault_classification.zip`,
+`unsupervised_anomaly_detection.zip`). This workshop only uses whole UAV scene
+images, so only `InsPLAD-det.zip` is extracted; the other two stay zipped and
+untouched.
 
-#### Annotation process
+```python
+"""Step 1: Download InsPLAD from source and extract only the detection
+(InsPLAD-det) sub-dataset: full UAV scene images, no cropped fault/anomaly
+images. This workshop uses whole images only.
+
+Source: Mendeley Data, https://data.mendeley.com/datasets/5n3fjgvfyz/1
+The Mendeley record ships one outer zip containing three inner zips
+(InsPLAD-det.zip, supervised_fault_classification.zip,
+unsupervised_anomaly_detection.zip). We download the outer zip (it's a
+single file on Mendeley, can't be split at the API level), but only extract
+InsPLAD-det.zip from it; the other two are left zipped and untouched.
+"""
+import zipfile
+from pathlib import Path
+
+import requests
+
+MENDELEY_DATASET_ID = "5n3fjgvfyz"
+WORK_DIR = Path(__file__).parent / "data"
+OUTER_ZIP = WORK_DIR / "InsPLAD_Dataset.zip"
+DET_DIR = WORK_DIR / "InsPLAD-det"
+
+
+def get_download_url():
+    """Query the Mendeley public API for the current file download URL
+    (avoids hardcoding a URL that may rotate)."""
+    resp = requests.get(
+        f"https://data.mendeley.com/public-api/datasets/{MENDELEY_DATASET_ID}"
+        "/files?folder_id=root&version=1"
+    )
+    resp.raise_for_status()
+    files = resp.json()
+    assert len(files) == 1, f"expected 1 file, got {len(files)}"
+    return files[0]["content_details"]["download_url"], files[0]["size"]
+
+
+def extract_det_only(outer_zip, det_dir):
+    with zipfile.ZipFile(outer_zip) as outer:
+        names = outer.namelist()
+        det_zip_name = next(n for n in names if n.endswith("InsPLAD-det.zip"))
+        outer.extract(det_zip_name, WORK_DIR)
+
+    inner_zip_path = WORK_DIR / det_zip_name
+    with zipfile.ZipFile(inner_zip_path) as inner:
+        inner.extractall(det_dir)
+    inner_zip_path.unlink()  # don't need the intermediate inner zip anymore
+```
 
-<!-- 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. -->
+Result: `data/InsPLAD-det/{train,val}/*.jpg` plus COCO annotation JSONs: 10,561
+unique images (46 duplicate COCO `image_id` entries for the same file are a known
+quirk of the source data, resolved during staging).
 
-[More Information Needed]
+#### Step 2: Build the balanced stratified sample
 
-#### Who are the annotators?
+```python
+"""Step 2: Build the reproducible, balanced stratified workshop pool
+manifest from InsPLAD-det's raw images. Whole scene images only, no
+labels attached to the resulting pool: ground truth for the sampled
+images is saved separately in step 3, held out for the "close the loop"
+act.
+
+Tiers:
+  0. Eval holdout: a stratified ~25-per-class slice across all 4 target
+     classes, carved out FIRST, before any other tier is built. These
+     images are never available to prioritization or annotation in any
+     downstream act -- they exist purely so there's a clean, never-touched
+     benchmark to evaluate the fine-tuned detector against later. Tagged
+     `eval_holdout` at import time (step 4).
+  1. Balanced annotation targets: capped per-flight, per-class samples of
+     all 4 target classes (`tower id plate`, `polymer insulator`,
+     `glass insulator`, `yoke`), excluding whatever tier 0 already claimed
+     for eval, each capped at the same quota so the pool is balanced
+     across classes instead of skewed toward whichever ones are naturally
+     most common. The quota is set by `tower id plate`'s natural ceiling
+     (only 242 images exist in all of InsPLAD-det, 25 of which tier 0
+     already claimed), so every class gets an equal shot at the
+     annotation budget.
+  2. Duplicate-wall flights: N whole flights kept 100% intact, giving the
+     "compress" act a real wall of near-identical drone frames to find
+     (not simulated: these are actual contiguous DJI frame sequences).
+  3. Long-tail texture: one image per remaining flight, so the embedding
+     plot's messy middle still looks like a messy middle.
+
+Deterministic given SEED: same manifest every run, same code whether
+this runs live at the workshop or at home.
+"""
+import random
+import re
+from collections import defaultdict
+
+SEED = 51
+N_DUP_WALL_FLIGHTS = 14
+TARGET_CLASSES = ["tower id plate", "polymer insulator", "glass insulator", "yoke"]
+EVAL_HOLDOUT_PER_CLASS = 25
+TARGET_CLASS_QUOTA = 217
+TARGET_CLASS_PER_FLIGHT_CAP = 4
+
+FLIGHT_PATTERN = re.compile(r"^(.+?)_DJI_(\d+)\.jpg$", re.IGNORECASE)
+rng = random.Random(SEED)
+
+
+def flight_of(filename):
+    """Images are named `<flight_id>_DJI_<frame>.jpg`; grouping by
+    flight_id recovers each drone's actual, contiguous flight sequence."""
+    m = FLIGHT_PATTERN.match(filename)
+    return m.group(1) if m else None
+
+
+# Tier 0: eval holdout, carved out FIRST and stratified per class, before
+# any annotation tier gets a chance to claim these images.
+for cls in TARGET_CLASSES:
+    candidates = [fn for fn, lbls in fn_labels.items() if cls in lbls and fn not in selected]
+    rng.shuffle(candidates)
+    picked = candidates[:EVAL_HOLDOUT_PER_CLASS]
+    for fn in picked:
+        selected[fn] = f"eval_holdout:{cls}"
+
+# Tier 1: all 4 target classes, capped at the same per-flight, per-class quota
+for cls in TARGET_CLASSES:
+    candidates = [fn for fn, lbls in fn_labels.items() if cls in lbls and fn not in selected]
+    rng.shuffle(candidates)
+    per_flight_count = defaultdict(int)
+    picked = []
+    for fn in candidates:
+        fid = flight_of(fn)
+        if per_flight_count[fid] < TARGET_CLASS_PER_FLIGHT_CAP and len(picked) < TARGET_CLASS_QUOTA:
+            picked.append(fn)
+            per_flight_count[fid] += 1
+
+# Tier 2: N_DUP_WALL_FLIGHTS whole flights, every frame kept
+flight_ids_sorted = sorted(flights.keys())
+dup_wall_flights = rng.sample(flight_ids_sorted, N_DUP_WALL_FLIGHTS)
+
+# Tier 3: one remaining image per flight, for long-tail texture
+for fid, fns in flights.items():
+    remaining = [fn for fn in fns if fn not in selected]
+    if remaining:
+        pick = rng.choice(remaining)
+```
 
-<!-- This section describes the people or systems who created the annotations. -->
+Result, with `seed=51`:
 
-[More Information Needed]
+| Tier | What it keeps | Images |
+|---|---|---|
+| 0: Eval holdout (25/class, carved out first) | `tower id plate`, `polymer insulator`, `glass insulator`, `yoke`, 25 each | 100 |
+| 1: Balanced annotation targets (capped per class, per flight) | `tower id plate` (214), `polymer insulator` (217), `glass insulator` (217), `yoke` (217) | 865 |
+| 2: Duplicate-wall flights (14 flights, 100% intact) | real contiguous drone-frame sequences | 574 |
+| 3: Long-tail texture (1/remaining flight) | everything else, thinly | 215 |
+| **Total** | | **1,754** |
 
-#### Personal and Sensitive Information
+The 100 eval-holdout images are tagged `eval_holdout` at import time (step 4)
+and excluded from every interactive step of the companion workshop; the true
+interactive pool at any point is 1,654 images. A later staging step copies
+all 1,754 images into a lean pool directory and converts their real COCO
+boxes to FiftyOne's relative `[x, y, w, h]` format, but writes them to
+`heldout_ground_truth.json` rather than into the FiftyOne dataset, which is
+imported strictly media-only.
 
-<!-- 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. -->
+### Source Data
 
-[More Information Needed]
+#### Data Collection and Processing
 
-## Bias, Risks, and Limitations
+The underlying images were captured by UAV (drone) during real-world inspections
+of operating power lines, at 1920x1080 resolution, under varied environmental
+conditions, orientations, and distances. See the original
+[InsPLAD dataset card](https://huggingface.co/datasets/harpreetsahota/InsPLAD)
+for the full collection and annotation process. This derivative pool applies no
+further transformation to the images themselves; it only selects which 1,754 of
+the 10,561 to include, per the stratified sampling above.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+#### Who are the source data producers?
 
-[More Information Needed]
+UAV inspection imagery of real, operating power lines, collected by the Voxar
+Labs group at Universidade Federal de Pernambuco (see the original InsPLAD paper
+and dataset card).
 
-### Recommendations
+### Annotations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+This dataset ships with no annotations. The real bounding boxes for these same
+1,754 images exist (extracted from InsPLAD-det's COCO annotations during
+staging) but are deliberately withheld from this Hub dataset, distributed
+alongside the workshop's companion code as `heldout_ground_truth.json` instead.
 
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+#### Personal and Sensitive Information
 
-## Citation [optional]
+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]`
 
-<!-- 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},
+}
+```
 
 **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 is a derivative sampling of InsPLAD-det for the "Cold Pool to Hot Queue"
+FiftyOne workshop. The full pipeline that builds this pool from scratch (source
+download through media-only import), plus the in-App workshop steps and the
+eval-holdout reveal/scoring script, lives in the workshop's companion GitHub
+repository: **https://github.com/harpreetsahota204/annotation_workshop/tree/main**.
+For the full, labeled InsPLAD dataset (all three official sub-tasks, 49,706
+samples), see https://huggingface.co/datasets/harpreetsahota/InsPLAD.
 
-## Dataset Card Authors [optional]
+## Dataset Card Authors
 
-[More Information Needed]
+Harpreet Sahota (FiftyOne / Voxel51 sampling and card)
 
 ## Dataset Card Contact
 
-[More Information Needed]
+harpreetsahota