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---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
- image-to-image
- object-detection
tags:
- code
- biology
dataset_info:
features:
- name: id
dtype: int32
- name: name
dtype: string
- name: image
dtype: image
- name: mask
dtype: image
- name: width
dtype: uint16
- name: height
dtype: uint16
- name: shapes
sequence:
- name: label
dtype:
class_label:
names:
'0': strawberry
- name: type
dtype: string
- name: points
sequence:
sequence: float32
- name: rotation
dtype: float32
- name: attributes
sequence:
- name: name
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 127730244
num_examples: 40
download_size: 126412271
dataset_size: 127730244
---
# Ripe Strawberries Object Detection dataset
The dataset consists of photos of strawberries for the identification and recognition of **ripe berries**.
The images are annotated with **bounding boxes** that accurately demarcate the location of the ripe strawberries within the image.
# 💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on **[TrainingData](https://trainingdata.pro/datasets?utm_source=huggingface&utm_medium=cpc&utm_campaign=ripe-strawberries-detection)** to buy the dataset
Each image in the dataset showcases a strawberry plantation, and includes a diverse range of *backgrounds, lighting conditions, and orientations*. The photos are captured from various *angles and distances*, providing a realistic representation of strawberries.
The dataset can be utilised for enabling advancements in *strawberry production, quality control, and greater precision in agricultural practices*.
# Dataset structure
- **images** - contains of original images of strawberries
- **boxes** - includes bounding box labeling for the original images
- **annotations.xml** - contains coordinates of the bounding boxes and labels, created for the original photo
# Data Format
Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes for ripe strawberries detection. For each point, the x and y coordinates are provided. Visibility of the ripe strawberry is also provided by the attribute **occluded** (0, 1).
# Example of XML file structure
# Strawberry Detection might be made in accordance with your requirements.
# 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[https://trainingdata.pro/datasets](https://trainingdata.pro/datasets?utm_source=huggingface&utm_medium=cpc&utm_campaign=ripe-strawberries-detection)** to discuss your requirements, learn about the price and buy the dataset
## **[TrainingData](https://trainingdata.pro/datasets?utm_source=huggingface&utm_medium=cpc&utm_campaign=ripe-strawberries-detection)** provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
*keywords: strawberry harvesting, ripeness, strawberry classification, mature strawberries, unripe, raw, overripe, detection system, harvestation stages, pluck strawberries, strawberry identification, berries, plantations, agriculture, mature fruit, greenhouse strawberries, recognition accuracy, flowers, software development, image dataset, segmentation, object detection, bounding boxes* |