{"id":2354,"date":"2021-04-14T12:23:20","date_gmt":"2021-04-14T17:23:20","guid":{"rendered":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/?page_id=2354"},"modified":"2021-04-20T02:22:39","modified_gmt":"2021-04-20T07:22:39","slug":"deliverables-08b","status":"publish","type":"page","link":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/deliverables\/deliverables-08b\/","title":{"rendered":"Deliverables 08b"},"content":{"rendered":"<p><em>This set of Deliverables is Due next Wednesday, April 21. It has two components:\u00a0<\/em><\/p>\n<ol>\n<li style=\"list-style-type: none;\">\n<ol>\n<li>ML Image Processor<\/li>\n<li>Situated Eye<\/li>\n<li>Install Unity, <strong><a href=\"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/daily-notes\/04-21-unity\/unity-1\/\">per the instructions here<\/a><\/strong>.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<hr \/>\n<h3>ML Image Processor<\/h3>\n<header class=\"entry-header\">Use your choice of {RunwayML, EbSynth, or Photoshop Neural Filters} to conduct a brief investigation that results in a media object that interests you. Don&#8217;t spend more than 2 hours on this.<\/header>\n<header><\/header>\n<header><strong>Here&#8217;s<a href=\"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/daily-notes\/04-14-ml-part-2\/some-ml-tools\/\"> the presentation I gave about these ML tools<\/a>.<\/strong><\/header>\n<header><\/header>\n<header class=\"entry-header\"><em>Now:<\/em><\/p>\n<ul>\n<li><strong>Create<\/strong> a blog post, titled <em>nickname-ImageProcessor,<\/em>\u00a0and categorized,\u00a0<em>08-ImageProcessor<\/em><\/li>\n<li><strong>Include<\/strong> a written description of your project, of 100 words, including a discussion of your process and some thoughts about your results.<\/li>\n<li><strong>Embed<\/strong> visual documentation of your project, such as before\/after images, animated GIFs, etc. so as to make clear what you made and how you made it.<\/li>\n<\/ul>\n<hr \/>\n<h3>Situated Eye<\/h3>\n<\/header>\n<div class=\"entry-content\">\n<p>You are asked to use Google\u2019s <strong><a href=\"https:\/\/teachablemachine.withgoogle.com\/\">Teachable Machine<\/a><\/strong> tool, which allows you to train a (neural-network-based) image recognition system, in the browser. This is intended to be the main part of Deliverables 08b, and might take you 2-5 hours.<\/p>\n<ul>\n<li>Train a detector using your computer\u2019s webcam and Google\u2019s\u00a0<strong><a href=\"https:\/\/teachablemachine.withgoogle.com\/\">Teachable Machine<\/a>.\u00a0<\/strong><\/li>\n<li><strong><em>Optionally<\/em><\/strong>, create an interactive system in p5.js that uses your detector as an input. (<em>For example, my <strong><a href=\"https:\/\/editor.p5js.org\/golan\/sketches\/zFm_Z-2Tw\">simple example project<\/a><\/strong> was based off of the template code provided by Teachable Machine when I trained my model.<\/em>)<\/li>\n<li>Record an animated GIF of your detector and\/or system in action.<\/li>\n<li>Embed your GIF in a blog post entitled\u00a0<em>nickname-TeachableMachine<\/em>, and categorize your blog post with the WordPress category,\u00a0<em>08-TeachableMachine<\/em>.<\/li>\n<li>In your blog post, write a reflective sentence or two about your experience using this tool.<\/li>\n<\/ul>\n<p>In this project, you are invited to consider how you can\u00a0 create a &#8220;situated eye&#8221; &#8211; a &#8220;contextualized classifier&#8221; &#8211; a &#8220;purposeful detector&#8221; &#8211; a &#8220;poetic surveillant&#8221;. Ideally, you will create a camera-based system:<\/p>\n<ul>\n<li>which is located in a specific place;<\/li>\n<li>which is\u00a0<em>trained<\/em>\u00a0to detect a specific thing (or things);<\/li>\n<li>and (<strong><em>optionally<\/em><\/strong>, with p5.js) which <em>responds<\/em> to what it sees, in an interesting way.<\/li>\n<\/ul>\n<h5>Design Considerations<\/h5>\n<ul>\n<li><em>Consider escaping the typical physical context of the laptop.<\/em>\u00a0Don&#8217;t limit yourself to the physical constraints of your laptop&#8217;s webcam, and the implicit assumptions it imposes on where a camera can be (on a table, in a room, at waist height, with a level pitch). If necessary, borrow a peripheral USB camera and a camera mount.<\/li>\n<li>Further to this point: Give <em>extremely careful consideration<\/em> to\u00a0<em>where<\/em>\u00a0your camera is located, and\/or what it observes. Is your camera on a building? In a refrigerator? Above a pet dish? Part of a microscope? Pointed at the sky, or at the floor? Looking at custom cardboard game pieces on a table? Observing objects on a conveyor belt? This is not a speculative matter; actually\u00a0<em>do the thing<\/em>.<\/li>\n<li>Your system might respond to the actions of a live interacting user (i.e. in the manner of game controller), or it might respond to people, vehicles, animals, or other phenomena that are wholly unaware that they are being observed. <em>*It is understood that you will not violate anyone&#8217;s privacy.\u00a0<\/em><\/li>\n<li>Your system might respond in real-time, or it might serve as a system for recording, logging, or counting what it observes. Keep in mind that you can save files (data, images) to disk&#8230;<\/li>\n<\/ul>\n<h5>List of Deliverables:<\/h5>\n<ul>\n<li>A blog post, titled\u00a0<em>nickname-SituatedEye,<\/em>\u00a0and categorized,\u00a0<em>08b-SituatedEye<\/em><\/li>\n<li>A written description of your project, 100-200 words, including an evaluation of your project.<\/li>\n<li>A brief demonstration video, embedded in the post (uploaded, or linked to YouTube or Vimeo)<\/li>\n<li>At least 3 pieces of visual documentation of your project, consisting (as appropriate and\/or possible) of photos\/scans of your notebook sketches; photographs of the project in situ; screenshots of the project; technical diagrams; example images from your training data, etc.<\/li>\n<li>An animated GIF<\/li>\n<\/ul>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>This set of Deliverables is Due next Wednesday, April 21. It has two components:\u00a0 ML Image Processor Situated Eye Install Unity, per the instructions here. ML Image Processor Use your choice of {RunwayML, EbSynth, or Photoshop Neural Filters} to conduct a brief investigation that results in a media object that interests you. Don&#8217;t spend more &hellip; <a href=\"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/deliverables\/deliverables-08b\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Deliverables 08b&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":15,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2354","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/wp-json\/wp\/v2\/pages\/2354","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/wp-json\/wp\/v2\/comments?post=2354"}],"version-history":[{"count":10,"href":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/wp-json\/wp\/v2\/pages\/2354\/revisions"}],"predecessor-version":[{"id":2572,"href":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/wp-json\/wp\/v2\/pages\/2354\/revisions\/2572"}],"up":[{"embeddable":true,"href":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/wp-json\/wp\/v2\/pages\/15"}],"wp:attachment":[{"href":"http:\/\/www.courses.art.cmu.edu\/2021s\/60210a\/wp-json\/wp\/v2\/media?parent=2354"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}