README.md

---
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

```python
from datasets import load_dataset

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

### Use with Nectar SDK

```python
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
```