README.md

---
license: apache-2.0
task_categories:
  - object-detection
tags:
  - object-detection
  - drone
  - uav
  - object-detection
  - target-detection
  - cross-target
  - nectar-sdk
size_categories:
  - 1K<n<10K
pretty_name: TargetCross 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': targetcross
                '1': targetcrossantigo
        - name: area
          sequence: float64
---

# TargetCross Detection Dataset

Object detection dataset for cross-shaped target detection. Single class: 'TargetCrossAntigo'.

## Dataset Structure

| Split | Images |
|-------|--------|
| train | 700 |
| validation | 300 |

**Total images:** 1000

**Classes:** `targetcross`, `targetcrossantigo`

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