harpreetsahota/InsPLAD-workshop-pool

Dataset Card for InsPLAD Workshop Pool

This is a FiftyOne dataset with 2227 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("harpreetsahota/InsPLAD-workshop-pool")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

InsPLAD Workshop Pool is a 2,227-image, media-only sample of InsPLAD-det, built to teach a rare-class data curation workflow in FiftyOne: compress a raw image pool, embed it, mine a rare 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 find: a genuine rare class, genuine near-duplicate sequences, and genuine common-class clusters. See Curation Rationale below for exactly how, and the original scripts under workshop-notebook/ in the source repository companion materials for the full, runnable pipeline.

• 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

Direct Use

Practicing (or teaching) a rare-class data curation loop end to end: near-duplicate compression, embedding-based visual exploration, seeded similarity search for a rare 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

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 is a flat image dataset (media_type = "image"), not grouped or video, with 2,227 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.

Fields

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

dataset.info

{
    "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."
    ),
}

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 (rare target, duplicate-wall, common-class, 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 the FiftyOne dataset's fields. That keeps the pool looking like a genuine unlabeled pool, not a labeled one with fields hidden.
• 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.

Dataset Creation

Curation Rationale

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 before checking rare-class coverage and a 242-image class can nearly vanish from a random slice. Instead, this pool uses a deterministic, 4-tier stratified sample (seed=51, same result every run) that guarantees every step of the workflow has something real to work with, at roughly a fifth of the source data's size.

Two scripts build this pool from the original InsPLAD-det source; both are included verbatim below for full reproducibility.

Step 1: Download InsPLAD-det from source

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.

"""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

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).

Step 2: Build the 4-tier stratified sample

"""Step 2: Build the reproducible, 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:
  1. Rare target: ALL images containing `tower id plate` (kept 100%).
     ~1% of images, ~99% of them the dominant subject in frame.
  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. Common-class coverage: capped per-flight samples of `polymer insulator`,
     `glass insulator`, `yoke`, enough for clean embedding clusters
     without needing thousands of images per class.
  4. 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
COMMON_CLASS_QUOTA = 450
COMMON_CLASS_PER_FLIGHT_CAP = 4
COMMON_CLASSES = ["polymer insulator", "glass insulator", "yoke"]

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 1: every image containing the rare target, kept 100%
tier1 = {fn for fn, lbls in fn_labels.items() if "tower id plate" in lbls}

# 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: per-flight-capped samples of each common class
for cls in COMMON_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] < COMMON_CLASS_PER_FLIGHT_CAP and len(picked) < COMMON_CLASS_QUOTA:
            picked.append(fn)
            per_flight_count[fid] += 1

# Tier 4: 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)

Result, with seed=51:

Tier	What it keeps	Images
1: Rare target (tower id plate, 100%)	every image containing the rare class	242
2: Duplicate-wall flights (14 flights, 100% intact)	real contiguous drone-frame sequences	562
3: Common-class coverage (capped per flight)	polymer insulator (450), yoke (450), glass insulator (310)	1,210
4: Long-tail texture (1/remaining flight)	everything else, thinly	213
Total		2,227

A later staging step copies these 2,227 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.

Source Data

Data Collection and Processing

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 for the full collection and annotation process. This derivative pool applies no further transformation to the images themselves; it only selects which 2,227 of the 10,561 to include, per the stratified sampling above.

Who are the source data producers?

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).

Annotations

This dataset ships with no annotations. The real bounding boxes for these same 2,227 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.

Personal and Sensitive Information

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]

Citation

BibTeX:

@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:

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

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) lives in the workshop-notebook/ directory of the workshop's companion materials, as five numbered, reproducible scripts. For the full, labeled InsPLAD dataset (all three official sub-tasks, 49,706 samples), see https://huggingface.co/datasets/harpreetsahota/InsPLAD.

Dataset Card Authors

Harpreet Sahota (FiftyOne / Voxel51 sampling and card)

Dataset Card Contact

harpreetsahota