---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: int64
    - name: categories
      list:
        class_label:
          names:
            '0': avocado
            '1': rockmelon
            '2': apple
            '3': orange
            '4': strawberry
            '5': mango
            '6': capsicum
  splits:
  - name: train
    num_bytes: 549952890
    num_examples: 565
  download_size: 549981336
  dataset_size: 549952890
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Fruit Detection Worldwide

A dataset for object detection of various fruits. The dataset contains 565 images with 3,132 bounding box annotations across 7 categories.

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

## Citation

```bibtex
@Article{s16081222,
AUTHOR = {Sa, Inkyu and Ge, Zongyuan and Dayoub, Feras and Upcroft, Ben and Perez, Tristan and McCool, Chris},
TITLE = {DeepFruits: A Fruit Detection System Using Deep Neural Networks},
JOURNAL = {Sensors},
VOLUME = {16},
YEAR = {2016},
NUMBER = {8},
ARTICLE-NUMBER = {1222},
URL = {https://www.mdpi.com/1424-8220/16/8/1222},
ISSN = {1424-8220},
DOI = {10.3390/s16081222}
}
```