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ML Image Identifier Lite Sur iTunes

This ML model is an example of fairly high-quality results in image recognition and is much more compact than similar ML models that can be as large as 500MB.  This ML model is an example of poor results in image recognition when used outside of very specific cases.  With the release of iOS 11, Apple brought machine learning to the masses with CoreML, making it possible to run neural networks and other ML-related tools via hardware acceleration on any iOS device.  This mode in particular works better on a newer device at a usable framerate, due to the hardware required for real-time image processing.  We see it in numerous uses, such as handwriting recognition, facial recognition, image tagging, AI in games, targeted advertisements, predictive typing, and many automated tasks.  For a ML model to work, it must be fed massive amounts of test data (similarly to how it takes a living creature numerous stimuli to learn).  This ML model is an example of mixed results in image recognition.  ML Image Identifier is an educational app that allows your iOS device to identify images in real-time, as you move the camera around your environment.  Social networks are free because the data (i.e. text, images, survey responses, etc.) you provide can be valuable for numerous purposes.  It can scan for 3 categories of images ("Objects", "Cars", and "Food") and recognize "Text" (character boxes, OCR) and "Faces" (feature landmarks).  The text-recognition mode looks for all potential text in view and highlights the words and individual characters in those words for easy viewing.  The app automatically throttles the image processing to work on any device running iOS 12, though it may be sluggish on older devices.  For the categorized images, the app displays the top-5 predicted matches, based on the neural networks' confidence levels as percentages.  Good test data can yield good results; poor test data can yield poor results.  It rarely works with general food items and seems to focus on foods that most people will not have in their houses, such as caviar and lobster.  Devices running iOS 13 additionally have optical character-recognition (OCR) in "Text" mode.  The facial-recognition mode looks for all potential human or human-like faces.  Sometimes, biases of those creating the tests can come into play, since they may unknowingly weigh certain test values over others.  If you enjoy the facial-recognition, consider HullBreach Studios' game "Exprestive", which is also available in the App Store.  Once merely a subject of science-fiction, machine learning has permeated our lives in recent decades.  This app is a demonstration of some possibilities - and some deficiencies - of machine learning.  It is very hit-or-miss and seems to heavily match automobiles from specific regions of the world.  Of those found, the app highlights the facial landmarks, such as eyes, nose, jawline, etc.  "CarRecognition" - This scans for makes and models of vehicles.  "MobileNet" - This scans general objects.  "Food101" - This scans for prepared foods.  Modeling a neural network is only one part of the task.  It works fairly well with household items.  If you enjoy this app, please consider ad-free version.  Most matches are the right body type but wrong make.  It cannot identify people.