Launchbar resize image12/2/2023 ![]() ![]() We call them app launchers, but the truth is they’re so much more. As I went about setting up LaunchBar again, it got me thinking how little I use this powerful tool. But I’ve personally been a long time LaunchBar user, after playing with Quicksilver in its early years.Īs I upgraded to macOS High Sierra this fall, I noticed the update remove all my login items for my user profile. Here at The Sweet Setup, Alfred is the launcher of choice. One category of Mac software that has always been an immediate install on a new machine has been the app launcher. It was not only better looking, but the form and function were both far better than what I was used to.Īnd yet it wasn’t long before I wanted to make use of many 3rd party tools to improve the experience. macOS (aka OS X) is what drew me to make the switch from the world of Windows, with its sleek look and brushed metal. If you had output the layer map and need to use it more than once, add a Set Memory Image #X after Apply Deep Learning and then use Call Memory Image #X as often as you need to select all Layers.The operating system is what sets Apple products apart from others. If you had output a probability map, use Deep Learning > Call Output to call other probability maps if needed. This sets the lowest probability percentage that counts as a feature.Īdd any additional steps needed to complete the feature detection. ![]() When added after an Apply Deep Learning step, Basic Threshold will auto-launch with Type = “Percentage” and Selection = “Bright”. This is often critical to achieve accurate feature detection.Īfter outputting the probability map, select the features of interest with Segmentation > Basic Threshold. * Tip: A key capability of working with deep learning in MIPAR is the ability to further process the probability map(s) or layer map after output. Output Resultįrom the Output dropdown choose either the layer map, or one of the probability maps. See Apply Model documentation for more details. * Tip: The “Size Factor” box is auto-populated with the size factor used to train the model. Launch the Image Processor from the Launch Bar.Ĭlick Load Model and choose the saved. This will auto-launch the Image Processor, auto-launch Deep Learning > Apply Model, and auto-load and apply the model. If you have not saved the model, you may click the arrow next to “Save Model” and choose Save and Apply Model. ssn2 file in AI Session Processor)Īfter training is complete and you have saved the model, you may auto-apply the model to the active Reference Image by clicking Apply Model. You may download the sample session file, reference images, recipe, and model to use as learning tools and follow this example. If you have not followed that example, you may download the session file below and open it in the AI Session Processor. It assumes a model has been trained as shown in the training example. This example shows how to apply a deep learning model to a new image in the Image Processor. The rest of MIPAR’s processing library can then take over to accurately select features. Effectively, application of a deep learning model results in a pre-processed version of the original image, where features of interest are “lit-up” against a dark background. Once a deep learning model is trained, it can be applied to a new image to automate feature detection. ![]() Session Processor / AI Session Processor.Notes, Flags, Interruptible, Custom Recipe Names.AI Session Processor (formerly Deep Learning Trainer).Session Processor (formerly Post Processor). ![]()
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