What is the Fruits by YOLO Dataset?

It is a collection of fruit images annotated in multi-class classification format for object detection and computer vision tasks.

How can this dataset be used in AI and ML projects?

It can be used for training fruit detection models, multi-class classification, object recognition, and AI experimentation.

Are the fruit samples diverse?

Yes, the dataset includes multiple fruit types captured under varying angles, lighting conditions, and backgrounds.

Does GTS provide annotation and labeling services?

Yes, GTS offers precise annotation and labeling services to enhance dataset usability for supervised AI training.

Is the dataset compliant with industry standards and privacy regulations?

Yes, all data collection and processing follow GDPR, HIPAA, and ISO 9001 & 27001 standards.

Fruits by YOLO - Fruits Detection

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Fruits by YOLO - Fruits Detection

Datasets

File

Fruits by YOLO

Use Case

Computer Vision

Description

Fruits by YOLO Image Dataset

About Dataset - Fruit Detection

The dataset includes Fruit Detection 2974 images.

Fruits are annotated in Multi-Class Classification format.

Explore the realm of fruits detection with our comprehensive YOLO Fruits Detection Dataset at Globose Technology Solutions. This dataset is specially curated to enhance machine learning models focused on accurately identifying and classifying various types of fruits in real-time. Whether you’re a researcher, a tech enthusiast, or a professional in the agricultural sector, our dataset provides the essential data needed for robust algorithms.

At GTS.ai, we understand the importance of high-quality, precise data for developing effective AI solutions. Our YOLO Dataset features a diverse range of fruit images captured under different conditions, ensuring versatility and adaptability of your models. Each image is meticulously annotated to provide not only the fruit type but also precise bounding box information for better object localization.

The following pre-processing was applied to each image:

Auto-orientation of pixel data (with EXIF-orientation stripping)
Resize to 640×640 (Stretch)

The following augmentation was applied to create 3 versions of each source image:

50% probability of horizontal flip
50% probability of vertical flip
Random rotation of between -15 and +15 degrees
Random shear of between -15° to +15° horizontally and -15° to +15° vertically
Random Gaussian blur of between 0 and 3.5 pixels

This dataset is sourced from Kaggle.

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Fruits by YOLO - Fruits Detection