iDetection 8.0.0

iDetection 8.0.0

Ultralytics LLC – Freeware – iOS
out of 51 votes
Latest Version
8.0.0
Safe to install
image/svg+xml VERY GOOD User Rating

YOLOv5 is an application that brings the power of advanced Artificial Intelligence (AI) to your iOS device camera. It utilizes cutting-edge technology to detect, classify, and locate up to 80 different types of common objects in real-time.

This impressive software has been trained on the COCO 2017 dataset using PyTorch and then exported to Apple CoreML through their open-source GitHub repository. This transparent approach ensures the credibility and reliability of the AI model.

With A14 iOS devices, YOLOv5 delivers impressive performance, offering over 30 frames per second at a default inference size of 192 x 320. However, it is important to note that older devices may experience slightly slower performance.

An important advantage of YOLOv5 is that all processing is done on-device. This means that images are not transmitted off-device, ensuring privacy and security.

For more information and in-depth details about YOLOv5, please visit their official GitHub repository at: https://github.com/ultralytics/yolov5

Overview

iDetection is a Freeware software in the category Audio & Multimedia developed by Ultralytics LLC.

The latest version of iDetection is 8.0.0, released on 02/11/2024. It was initially added to our database on 02/11/2024.

iDetection runs on the following operating systems: iOS.

Users of iDetection gave it a rating of 4 out of 5 stars.

Pros

  • Highly accurate object detection capabilities.
  • Real-time processing speeds suitable for dynamic environments.
  • User-friendly interface that simplifies deployment.
  • Pre-trained models available to save time and computational resources.
  • Flexible API that integrates easily with various applications and platforms.
  • Strong community support and extensive documentation available.
  • Regular updates with new features and optimizations.

Cons

  • Requires a good understanding of machine learning concepts for optimal use.
  • Can be resource-heavy, necessitating robust hardware for best performance.
  • Dependence on internet connectivity for cloud-based features.
  • Less effective in detecting small objects compared to larger ones.
  • Potentially high costs associated with heavy usage or enterprise-level features.

Screenshots (Click to view larger)

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