![]() ![]() Basic familiarity with TensorFlow.js, HTML5, CSS, and JavaScript is assumed for this lab.This codelab is written for web developers who are somewhat familiar with TensorFlow.js pre-made models and basic API usage, and who want to get started with transfer learning inTensorFlow.js. As with the original Teachable Machine website, however, there is plenty of scope to apply your existing web developer experience to improve the UX. The website is purposely kept minimal so that you can focus on the Machine Learning aspects of this codelab. The website lets you create a functional web app that any user can use to recognize a custom object with just a few example images from their webcam. This codelab shows you how to build a web app from a blank canvas, recreating Google's popular " Teachable Machine" website. For this reason, transfer learning is very well suited for the web browser environment where resources may vary based on the device of execution, but also has direct access to the sensors for easy data acquisition. Also, training is often significantly faster due to only having to retrain the final few layers of the model architecture instead of the whole network. You can reuse knowledge already learned from a prior trained model, and you require fewer examples of the new item you wish to classify. Transfer learning has many advantages over starting from a completely blank model. The act of taking an existing model (often referred to as a base model), and using it on a similar but different domain is known as transfer learning. A display specification that is capable of displaying 2048 x 1080 resolution, or approximately 2.2 million model usage has grown exponentially over the past few years and many JavaScript developers are now looking to take existing state-of-the-art models and retrain them to work with custom data that is unique to their industry. The sequence of single, alternating frames where each successive frame carries the image meant for either the right or left eye. The image is viewed by each eye at 60 frames per second (FPS). #Arraysync alternative full#Įach eye sees the full resolution of the image. This type of 3D is also known as frame alternative or page flip. 6Pĭeveloped primarily for 3D applications, 6P laser projection uses two sets of RGB lasers: one for the left eye, and one with slightly different wavelengths for the right eye.ģDLPĪ display specification that is capable of displaying 4096 x 2160 resolution, or approximately 8.85 million pixels. "6P" refers to these six primary colors of laser light, with three for each eye. 3D glasses filter the wavelengths, directing the light to the correct eye. Unlike traditional 3D, Christie’s 6P laser projection presents images simultaneously to the right and left eye. This eliminates the fatigue, headaches and nausea some viewers experience with traditional 3D projection, where our brains have to correct for the temporal offset created by images flashing sequentially to the right and left eye. The benefits of 6P include higher brightness, a wider range of color and detail, and a better viewing experience. 75-ohmĭeveloped specifically for the simulation market, AccuFrame nullifies image artifacts (such as smearing or double-image perception) in high speed simulation. A fully-adjustable electronic solution, AccuFrame supports various frame rates and environments, delivering accurate frame display. AccuFrame enables the removal of any perceived "double imaging" of content due to image frame perception in the eye. The time, inside one horizontal scan line, it takes to generate video. Additive color modelĪ projector model that uses red, green and blue light as the additive primary colors to produce the other colors. Combining one of the additive primary colors with another in equal amounts produces the additive secondary colors: cyan, magenta and yellow. Combining all three additive primary colors in equal intensities produces white. ![]()
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