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image image | image_id int64 | width int32 | height int32 | objects dict |
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End of preview.
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
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