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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}
}
``` |