Datasets:

blackbeedrones
/

sae-2026-manometer

license: apache-2.0
task_categories:
  - object-detection
tags:
  - object-detection
  - drone
  - uav
  - sae
  - manometer-detection
  - pressure-gauge
  - nectar-sdk
size_categories:
  - 10K<n<100K
pretty_name: SAE 2026 Manometer Detection Dataset
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: image_id
      dtype: int64
    - name: width
      dtype: int32
    - name: height
      dtype: int32
    - name: objects
      struct:
        - name: id
          sequence: int64
        - name: bbox
          sequence:
            sequence: float32
            length: 4
        - name: category
          sequence:
            class_label:
              names:
                '0': manometer-wvhf
                '1': manometer
        - name: area
          sequence: float64

SAE 2026 Manometer Detection Dataset

Object detection dataset for analog manometer (pressure gauge) detection used by Black Bee Drones in the SAE 2026 competition.

Dataset Structure

Split	Images
train	11422
validation	2438

Total images: 13860

Classes: manometer-wvhf, manometer

Annotation format: COCO bbox [x_min, y_min, width, height].

Usage

Load with HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("blackbeedrones/sae-2026-manometer")
example = dataset["train"][0]
print(example["objects"])

Use with Nectar SDK

from nectar.ai.detection.datasets import HuggingFaceHandler

handler = HuggingFaceHandler("data/local")
handler.download(repo_id="blackbeedrones/sae-2026-manometer", format_type="coco")
# data/local now contains train/_annotations.coco.json and image files