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Object Detection in iOS, xCode project using Google MLKit and Tensorflow Lite
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In this video we add the Object detection library with classification in the MLSeries Demonstrator.
With ML Kit's on-device object detection and tracking API, you can detect and track objects in an image or live camera feed.
Optionally, you can classify detected objects, either by using the coarse classifier built into the API, or using your own custom image classification model. See Using a custom TensorFlow Lite model for more information.
Because object detection and tracking happens on the device, it works well as the front end of the visual search pipeline. After you detect and filter objects, you can pass them to a cloud backend, such as Cloud Vision Product Search.
Key capabilities
1. Fast object detection and tracking Detect objects and get their locations in the image. Track objects across successive image frames.
Optimized on-device model The object detection and tracking model is optimized for mobile devices and intended for use in real-time applications, even on lower-end devices.
2. Prominent object detection Automatically determine the most prominent object in an image.
3. Coarse classification Classify objects into broad categories, which you can use to filter out objects you're not interested in. The following categories are supported: home goods, fashion goods, food, plants, and places.
4. Classification with a custom model Use your own custom image classification model to identify or filter specific object categories. Make your custom model perform better by leaving out background of the image.
#machinelearning
#mlkit
#objectdetection
#classification
Follow us for updates here:
The Mobile Dev YouTube Channel
The Mobile Dev - Twitter
With ML Kit's on-device object detection and tracking API, you can detect and track objects in an image or live camera feed.
Optionally, you can classify detected objects, either by using the coarse classifier built into the API, or using your own custom image classification model. See Using a custom TensorFlow Lite model for more information.
Because object detection and tracking happens on the device, it works well as the front end of the visual search pipeline. After you detect and filter objects, you can pass them to a cloud backend, such as Cloud Vision Product Search.
Key capabilities
1. Fast object detection and tracking Detect objects and get their locations in the image. Track objects across successive image frames.
Optimized on-device model The object detection and tracking model is optimized for mobile devices and intended for use in real-time applications, even on lower-end devices.
2. Prominent object detection Automatically determine the most prominent object in an image.
3. Coarse classification Classify objects into broad categories, which you can use to filter out objects you're not interested in. The following categories are supported: home goods, fashion goods, food, plants, and places.
4. Classification with a custom model Use your own custom image classification model to identify or filter specific object categories. Make your custom model perform better by leaving out background of the image.
#machinelearning
#mlkit
#objectdetection
#classification
Follow us for updates here:
The Mobile Dev YouTube Channel
The Mobile Dev - Twitter
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