Datasets:

blackbeedrones
/

targetcross-dataset

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

from datasets import load_dataset

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