As we continue to push the boundaries of what's possible with artificial intelligence (AI) in mobile apps, it's essential to explore the various machine learning projects that can help you get started. Whether you're a developer looking to add AI capabilities to your app or a hobbyist eager to experiment with this technology, there are numerous resources available to guide you along the way.

Image Recognition

One of the most popular AI-powered features in mobile apps is image recognition. This computer vision technique allows machines to interpret and categorize what they "see" in images or videos, making it an essential component in solving many machine learning problems.

From identifying objects as hotdogs (à la Not Hotdog) to recognizing skin conditions and handwritten digits, there are numerous demo projects that showcase the power of image recognition. For example:

  • Not Hotdog: Point your phone's camera at an object, and an AI-powered model will tell you whether or not it's a hotdog.
  • Skin Disease Recognition/Classification: Use on-device image recognition to determine if an individual has a particular skin condition.
  • Handwritten Digit Classification: Classify handwritten digits 0-9 in real-time using the famous MNIST dataset.

Object Detection

Another essential AI-powered feature is object detection, which allows us to identify and locate objects in an image or video. With this technology, we can count objects in a scene, determine their precise locations, and accurately label them.

Some popular demo projects for object detection include:

  • Object Detection for Individuals with Visual Impairments: Use object detection to recognize, locate, and track objects in the real world, providing non-visual warnings to users about impediments or moving objects.
  • Pet Monitoring: Monitor your pets while you're away by recognizing and tracking their movements.

Image Segmentation

Image segmentation is a computer vision technique that provides more fine-grain information about the contents of an image. Unlike image recognition, which assigns one or more labels to an entire image, image segmentation understands what's in a given image at a pixel level.

Some popular demo projects for image segmentation include:

  • Portrait Mode: Create a pixel-level mask of the foreground and blur the background to achieve a depth-of-field effect.
  • Green Screen Effect / Background Replacement: Isolate backgrounds in images and video feeds, allowing you to overlay graphics, AR objects, and more.

Pose Estimation

Pose estimation is a computer vision task that infers the pose of a person or object in an image or video. This technology can be used for human movement tracking, allowing us to identify and track keypoints on a given person or object.

Some popular demo projects for pose estimation include:

  • Human Movement Tracking: Use pose estimation to track the movements of humans and objects in real-time.

These AI-powered features have numerous applications across various industries, from healthcare and finance to entertainment and education. By experimenting with these machine learning projects, you can unlock new possibilities for your mobile app and stay ahead of the curve in this rapidly evolving field.