@react-native-firebase/ml-vision
Advanced tools
React Native Firebase - Firebase ML Kit brings the power of machine learning vision to your React Native application, supporting both Android & iOS.
This package is deprecated and should no longer be used.
Google has split mobile machine learning functionality into two pieces:
"On-Device" inferences - this will be handled via the standalone "Google ML Kit" libraries, and the related react-native-mlkit package. This includes any APIs where the device uses a local model to make inferences
"Cloud" inferences - these will continue in Firebase, but are now in the "Firebase ML" library, and will be available from the new consolidated @react-native-firebase/ml package
More information on the transition is available here: https://firebase.google.com/docs/ml#cloud_vs_on-device
Built and maintained with 💛 by Invertase.
FAQs
React Native Firebase - Firebase ML Kit brings the power of machine learning vision to your React Native application, supporting both Android & iOS.
The npm package @react-native-firebase/ml-vision receives a total of 49 weekly downloads. As such, @react-native-firebase/ml-vision popularity was classified as not popular.
We found that @react-native-firebase/ml-vision demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 4 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
In this segment of the Risky Business podcast, Feross Aboukhadijeh and Patrick Gray discuss the challenges of tracking malware discovered in open source softare.
Research
Security News
A threat actor's playbook for exploiting the npm ecosystem was exposed on the dark web, detailing how to build a blockchain-powered botnet.
Security News
NVD’s backlog surpasses 20,000 CVEs as analysis slows and NIST announces new system updates to address ongoing delays.
