This is the sequel to the previous post on How To Create Updatable Core ML Models With Core ML Tools Continue reading “How To Train Your Core ML Model On Device”
Core ML got a big boost this year with the Core ML 3 update during WWDC 2019. Among the many improvements, we got, On-Device Learning stands out. Continue reading “Core ML 3 Create Updatable Models”
We’ve already covered Cat vs Dog Image Classifier using our own Core ML model in a previous article.
With iOS 13, Vision is even more powerful.
VNImageRequest now has
VNRecognizeAnimalRequest to identify cats and dogs in images.
Continue reading “iOS Vision Animal Classifier Cat Vs Dog”
iOS 13 has finally rolled out for the public. In no time, 13.1 was out as well. I’m sure you’ll be shipping your next app updates with it. Before doing that let’s go through a checklist of essential things.
Continue reading “iOS 13 Checklist for developers”
Deep learning is a popular and interesting subset of Machine Learning. Deep learning brings neural networks into the limelight. Many complex tasks just as image classification, speech recognition etc can be achievable with the help of Deep Learning. We’ll be focusing on Image Classification only in this post.
Continue reading “iOS Cat and Dog Image Classifier With CoreML and Keras”
Previously, we had used Vision and Core ML to scan and recognize texts from an image.
Now that iOS 13 is here, the Vision API is vastly improved. Besides, VisionKit framework is now introduced which allows us to scan documents using Camera.
Continue reading “iOS 13 Vision Text Recognition with Document Scanner”
Recently I was asked to lock the device orientation to portrait only for an iOS Application.
Trusting Xcode blindly, I went to the Project Navigator -> Deployment Info and checked the portrait only mode Continue reading “Xcode Diaries #1 Device Orientation For iPhone, iPad”
Vision and Core ML frameworks were the highlights of WWDC 2017. Vision is a powerful framework used to implement computer vision features without much prior knowledge of algorithms.
Things such as barcode, face, object and text detection can be easily done using Vision.
At the same time, Core ML allows us to integrate and run pre-trained models without digging too deep in Machine Learning.