---
license: apache-2.0
task_categories:
  - object-detection
tags:
  - object-detection
  - drone
  - uav
  - object-detection
  - medical-delivery
  - package-detection
  - landing-pad
  - nectar-sdk
size_categories:
  - 1K<n<10K
pretty_name: MedicalKitDrone v3 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': base
                '1': package
        - name: area
          sequence: float64
---

# MedicalKitDrone v3 Detection Dataset

Object detection dataset for medical kit drone delivery missions. Two classes: 'Package' (the medical kit) and 'Base' (the landing/dropoff pad).

## Dataset Structure

| Split | Images |
|-------|--------|
| train | 1000 |
| validation | 250 |

**Total images:** 1250

**Classes:** `base`, `package`

**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/medicalkit-drone-dataset")
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/medicalkit-drone-dataset", format_type="coco")
# data/local now contains train/_annotations.coco.json and image files
```