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Datasets:
Project-AgML
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vineyard_grape_trunk_detection

Tasks:
Object Detection
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vineyard_grape_trunk_detection
File size: 3,115 Bytes
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---
dataset_info:
- config_name: augmented
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': trunk
            '1': tiny_grape_bunch
            '2': medium_grape_bunch
  - name: capture_set
    dtype: string
  - name: augmentation
    dtype: string
  splits:
  - name: train
    num_bytes: 2696503818
    num_examples: 18515
  download_size: 2694490286
  dataset_size: 2696503818
- config_name: raw
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': trunk
            '1': tiny_grape_bunch
            '2': medium_grape_bunch
  - name: capture_set
    dtype: string
  splits:
  - name: train
    num_bytes: 870691241
    num_examples: 2846
  download_size: 870301684
  dataset_size: 870691241
configs:
- config_name: augmented
  data_files:
  - split: train
    path: augmented/train-*
- config_name: raw
  data_files:
  - split: train
    path: raw/train-*
  default: true
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 1K<n<10K
---
# Vineyard Grape Trunk Detection

Images of vineyards in the Douro region of Portugal, taken by several ground-level cameras in varied illumination, with bounding boxes for vine trunks and for grape bunches at two growth stages (tiny, after bloom, and medium). The dataset accompanies a study on detecting grape bunches with quantized deep-learning models on low-power hardware; the current Zenodo version also adds trunk annotations. The raw config holds the 2,846 unaugmented RGB images; the augmented config holds 18,515 augmented copies (rotation, flipping, hue/saturation shifts, noise, scaling and translation), with the augmentation named per image. Each image records its capture set, and the record's thermal images are left out. The dataset contains raw and augmented versions.
The raw split contains 2,846 images with 13,218 bounding box annotations across 3 categories.  

The augmented split contains 18,515 images with 85,722 bounding box annotations across 3 categories.  


This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Citation

```bibtex
@article{aguiar2021grape,
  title={Grape Bunch Detection at Different Growth Stages Using Deep Learning Quantized Models},
  author={Aguiar, André Silva and Magalhães, Sandro Augusto and dos Santos, Filipe Neves and Castro, Luis and Pinho, Tatiana and Valente, João and Martins, Rui and Boaventura-Cunha, José},
  journal={Agronomy},
  volume={11},
  pages={1890},
  year={2021},
  publisher={MDPI}
}
```


The dataset itself can be cited as:

Aguiar, A. S., &amp; Magalhães, S. (2021). *Grape bunch and vine trunk dataset for Deep Learning object detection.* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.5139598


*This dataset was reformatted from its original format to match HuggingFace standards.*