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ML Image Identifier Sur iTunes

It can scan for 3 categories of images ("Objects", "Cars", and "Food") and recognize "Text" (character boxes, OCR) and "People" (facial landmarks, upper bodies, facial segmentation, depth map).  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.  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.  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.  This ML model is an example of mixed results in image recognition.  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.  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 you provide (e.g. posts, surveys, photos, etc.) can be valuable for numerous purposes, turning the users into the products to sell.  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.  For the categorized images, the app displays the top-5 predicted matches, based on the neural networks' confidence levels as percentages.  The "Text" mode looks for all potential text in view and highlights the words and individual characters in those words for easy viewing.  Good test data can yield good results; poor test data can yield poor results.  It also supports upper torso detection (back camera) and facial segmentation or depth map (front camera).  The app automatically throttles the image processing to work on older devices.  The "People" 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.  Of those found, the app highlights the facial landmarks, such as eyes, nose, jawline, etc.  Once merely a subject of science-fiction, machine learning has permeated our lives in recent decades.  It displays the top 5 rows of text by descending height.  It is very hit-or-miss and seems to heavily match automobiles from specific regions of the world.  This app is a demonstration of some possibilities - and some deficiencies - of machine learning.  "MobileNet" - This scans general objects.  "CarRecognition" - This scans for makes and models of vehicles.  It cannot identify people.  Modeling a neural network is only one part of the task.  "Food101" - This scans for prepared foods.  It works fairly well with household items.  Most matches are the right body type but wrong make.  Each subsequent iOS version added to the featureset.