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This Computer Vision Tutorial will introduce you Computer Vision and take you deep into concepts and practical implementation of the subject. Computer Vision is the ability of machine to have sight or a vision. It is the process of recording recording and playing back light fragments. It helps solve problems where you connect digital world to physical world. This video is a perfect started for people who are curious about Computer Vision.
Following Pointers will be covered in this session,
#opencv #machine-learning #python #developer
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This repository is a fork of SimpleMDE, made by Sparksuite. Go to the dedicated section for more information.
A drop-in JavaScript text area replacement for writing beautiful and understandable Markdown. EasyMDE allows users who may be less experienced with Markdown to use familiar toolbar buttons and shortcuts.
In addition, the syntax is rendered while editing to clearly show the expected result. Headings are larger, emphasized words are italicized, links are underlined, etc.
EasyMDE also features both built-in auto saving and spell checking. The editor is entirely customizable, from theming to toolbar buttons and javascript hooks.
Via npm:
npm install easymde
Via the UNPKG CDN:
<link rel="stylesheet" href="https://unpkg.com/easymde/dist/easymde.min.css">
<script src="https://unpkg.com/easymde/dist/easymde.min.js"></script>
Or jsDelivr:
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/easymde/dist/easymde.min.css">
<script src="https://cdn.jsdelivr.net/npm/easymde/dist/easymde.min.js"></script>
After installing and/or importing the module, you can load EasyMDE onto the first textarea
element on the web page:
<textarea></textarea>
<script>
const easyMDE = new EasyMDE();
</script>
Alternatively you can select a specific textarea
, via JavaScript:
<textarea id="my-text-area"></textarea>
<script>
const easyMDE = new EasyMDE({element: document.getElementById('my-text-area')});
</script>
Use easyMDE.value()
to get the content of the editor:
<script>
easyMDE.value();
</script>
Use easyMDE.value(val)
to set the content of the editor:
<script>
easyMDE.value('New input for **EasyMDE**');
</script>
true
, force downloads Font Awesome (used for icons). If set to false
, prevents downloading. Defaults to undefined
, which will intelligently check whether Font Awesome has already been included, then download accordingly.true
, focuses the editor automatically. Defaults to false
.true
, saves the text automatically. Defaults to false
.10000
(10 seconds).autosave.delay
or 10000
(10 seconds).locale: en-US, format: hour:minute
.{ delay: 300 }
, it will check every 300 ms if the editor is visible and if positive, call CodeMirror's refresh()
.**
or __
. Defaults to **
.```
or ~~~
. Defaults to ```
.*
or _
. Defaults to *
.*
, -
or +
. Defaults to *
.textarea
element to use. Defaults to the first textarea
element on the page.true
, force text changes made in EasyMDE to be immediately stored in original text area. Defaults to false
.false
, indent using spaces instead of tabs. Defaults to true
.false
by default, preview for images will appear only for images on separate lines.
as argument and returns a string that serves as the src
attribute of the <img>
tag in the preview. Enables dynamic previewing of images in the frontend without having to upload them to a server, allows copy-pasting of images to the editor with preview.["[", "](http://)"]
.true
, enables line numbers in the editor.false
, disable line wrapping. Defaults to true
."500px"
. Defaults to "300px"
.minHeight
option will be ignored. Should be a string containing a valid CSS value like "500px"
. Defaults to undefined
.true
when the editor is currently going into full screen mode, or false
.true
, will render headers without a space after the #
. Defaults to false
.false
, will not process GFM strikethrough syntax. Defaults to true
.true
, let underscores be a delimiter for separating words. Defaults to false
.false
, will replace CSS classes returned by the default Markdown mode. Otherwise the classes returned by the custom mode will be combined with the classes returned by the default mode. Defaults to true
."editor-preview"
.true
, a JS alert window appears asking for the link or image URL. Defaults to false
.URL of the image:
.URL for the link:
.true
, enables the image upload functionality, which can be triggered by drag and drop, copy-paste and through the browse-file window (opened when the user click on the upload-image icon). Defaults to false
.1024 * 1024 * 2
(2 MB).image/png, image/jpeg
.imageMaxSize
, imageAccept
, imageUploadEndpoint
and imageCSRFToken
ineffective.onSuccess
and onError
callback functions as parameters. onSuccess(imageUrl: string)
and onError(errorMessage: string)
{"data": {"filePath": "<filePath>"}}
where filePath is the path of the image (absolute if imagePathAbsolute
is set to true, relative if otherwise);{"error": "<errorCode>"}
, where errorCode can be noFileGiven
(HTTP 400 Bad Request), typeNotAllowed
(HTTP 415 Unsupported Media Type), fileTooLarge
(HTTP 413 Payload Too Large) or importError
(see errorMessages below). If errorCode is not one of the errorMessages, it is alerted unchanged to the user. This allows for server-side error messages. No default value.true
, will treat imageUrl
from imageUploadFunction
and filePath returned from imageUploadEndpoint
as an absolute rather than relative path, i.e. not prepend window.location.origin
to it.imageCSRFToken
has value, defaults to csrfmiddlewaretoken
.true
, passing CSRF token via header. Defaults to false
, which pass CSRF through request body.#image_name#
, #image_size#
and #image_max_size#
will replaced by their respective values, that can be used for customization or internationalization:uploadImage
is set to true
. Defaults to Attach files by drag and dropping or pasting from clipboard.
.Drop image to upload it.
.Uploading images #images_names#
.Uploading #file_name#: #progress#%
.Uploaded #image_name#
.B, KB, MB
(example: 218 KB
). You can use B,KB,MB
instead if you prefer without whitespaces (218KB
).errorCallback
option, where #image_name#
, #image_size#
and #image_max_size#
will replaced by their respective values, that can be used for customization or internationalization:You must select a file.
.imageAccept
list, or the server returned this error code. Defaults to This image type is not allowed.
.imageMaxSize
, or if the server returned this error code. Defaults to Image #image_name# is too big (#image_size#).\nMaximum file size is #image_max_size#.
.Something went wrong when uploading the image #image_name#.
.(errorMessage) => alert(errorMessage)
.true
, will highlight using highlight.js. Defaults to false
. To use this feature you must include highlight.js on your page or pass in using the hljs
option. For example, include the script and the CSS files like:<script src="https://cdn.jsdelivr.net/highlight.js/latest/highlight.min.js"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/highlight.js/latest/styles/github.min.css">
window.hljs
), you can provide an instance here. Defaults to undefined
.renderingConfig
options will take precedence.false
, disable parsing GitHub Flavored Markdown (GFM) single line breaks. Defaults to true
.false
, disable the spell checker. Defaults to true
. Optionally pass a CodeMirrorSpellChecker-compliant function.textarea
or contenteditable
. Defaults to textarea
for desktop and contenteditable
for mobile. contenteditable
option is necessary to enable nativeSpellcheck.false
, disable native spell checker. Defaults to true
.false
, allows side-by-side editing without going into fullscreen. Defaults to true
.false
, hide the status bar. Defaults to the array of built-in status bar items.false
, remove the CodeMirror-selectedtext
class from selected lines. Defaults to true
.false
, disable syncing scroll in side by side mode. Defaults to true
.2
.easymde
.false
, hide the toolbar. Defaults to the array of icons.false
, disable toolbar button tips. Defaults to true
.rtl
or ltr
. Changes text direction to support right-to-left languages. Defaults to ltr
.Most options demonstrate the non-default behavior:
const editor = new EasyMDE({
autofocus: true,
autosave: {
enabled: true,
uniqueId: "MyUniqueID",
delay: 1000,
submit_delay: 5000,
timeFormat: {
locale: 'en-US',
format: {
year: 'numeric',
month: 'long',
day: '2-digit',
hour: '2-digit',
minute: '2-digit',
},
},
text: "Autosaved: "
},
blockStyles: {
bold: "__",
italic: "_",
},
unorderedListStyle: "-",
element: document.getElementById("MyID"),
forceSync: true,
hideIcons: ["guide", "heading"],
indentWithTabs: false,
initialValue: "Hello world!",
insertTexts: {
horizontalRule: ["", "\n\n-----\n\n"],
image: [""],
link: ["[", "](https://)"],
table: ["", "\n\n| Column 1 | Column 2 | Column 3 |\n| -------- | -------- | -------- |\n| Text | Text | Text |\n\n"],
},
lineWrapping: false,
minHeight: "500px",
parsingConfig: {
allowAtxHeaderWithoutSpace: true,
strikethrough: false,
underscoresBreakWords: true,
},
placeholder: "Type here...",
previewClass: "my-custom-styling",
previewClass: ["my-custom-styling", "more-custom-styling"],
previewRender: (plainText) => customMarkdownParser(plainText), // Returns HTML from a custom parser
previewRender: (plainText, preview) => { // Async method
setTimeout(() => {
preview.innerHTML = customMarkdownParser(plainText);
}, 250);
return "Loading...";
},
promptURLs: true,
promptTexts: {
image: "Custom prompt for URL:",
link: "Custom prompt for URL:",
},
renderingConfig: {
singleLineBreaks: false,
codeSyntaxHighlighting: true,
sanitizerFunction: (renderedHTML) => {
// Using DOMPurify and only allowing <b> tags
return DOMPurify.sanitize(renderedHTML, {ALLOWED_TAGS: ['b']})
},
},
shortcuts: {
drawTable: "Cmd-Alt-T"
},
showIcons: ["code", "table"],
spellChecker: false,
status: false,
status: ["autosave", "lines", "words", "cursor"], // Optional usage
status: ["autosave", "lines", "words", "cursor", {
className: "keystrokes",
defaultValue: (el) => {
el.setAttribute('data-keystrokes', 0);
},
onUpdate: (el) => {
const keystrokes = Number(el.getAttribute('data-keystrokes')) + 1;
el.innerHTML = `${keystrokes} Keystrokes`;
el.setAttribute('data-keystrokes', keystrokes);
},
}], // Another optional usage, with a custom status bar item that counts keystrokes
styleSelectedText: false,
sideBySideFullscreen: false,
syncSideBySidePreviewScroll: false,
tabSize: 4,
toolbar: false,
toolbarTips: false,
});
Below are the built-in toolbar icons (only some of which are enabled by default), which can be reorganized however you like. "Name" is the name of the icon, referenced in the JavaScript. "Action" is either a function or a URL to open. "Class" is the class given to the icon. "Tooltip" is the small tooltip that appears via the title=""
attribute. Note that shortcut hints are added automatically and reflect the specified action if it has a key bind assigned to it (i.e. with the value of action
set to bold
and that of tooltip
set to Bold
, the final text the user will see would be "Bold (Ctrl-B)").
Additionally, you can add a separator between any icons by adding "|"
to the toolbar array.
Name | Action | Tooltip Class |
---|---|---|
bold | toggleBold | Bold fa fa-bold |
italic | toggleItalic | Italic fa fa-italic |
strikethrough | toggleStrikethrough | Strikethrough fa fa-strikethrough |
heading | toggleHeadingSmaller | Heading fa fa-header |
heading-smaller | toggleHeadingSmaller | Smaller Heading fa fa-header |
heading-bigger | toggleHeadingBigger | Bigger Heading fa fa-lg fa-header |
heading-1 | toggleHeading1 | Big Heading fa fa-header header-1 |
heading-2 | toggleHeading2 | Medium Heading fa fa-header header-2 |
heading-3 | toggleHeading3 | Small Heading fa fa-header header-3 |
code | toggleCodeBlock | Code fa fa-code |
quote | toggleBlockquote | Quote fa fa-quote-left |
unordered-list | toggleUnorderedList | Generic List fa fa-list-ul |
ordered-list | toggleOrderedList | Numbered List fa fa-list-ol |
clean-block | cleanBlock | Clean block fa fa-eraser |
link | drawLink | Create Link fa fa-link |
image | drawImage | Insert Image fa fa-picture-o |
table | drawTable | Insert Table fa fa-table |
horizontal-rule | drawHorizontalRule | Insert Horizontal Line fa fa-minus |
preview | togglePreview | Toggle Preview fa fa-eye no-disable |
side-by-side | toggleSideBySide | Toggle Side by Side fa fa-columns no-disable no-mobile |
fullscreen | toggleFullScreen | Toggle Fullscreen fa fa-arrows-alt no-disable no-mobile |
guide | This link | Markdown Guide fa fa-question-circle |
undo | undo | Undo fa fa-undo |
redo | redo | Redo fa fa-redo |
Customize the toolbar using the toolbar
option.
Only the order of existing buttons:
const easyMDE = new EasyMDE({
toolbar: ["bold", "italic", "heading", "|", "quote"]
});
All information and/or add your own icons
const easyMDE = new EasyMDE({
toolbar: [
{
name: "bold",
action: EasyMDE.toggleBold,
className: "fa fa-bold",
title: "Bold",
},
"italics", // shortcut to pre-made button
{
name: "custom",
action: (editor) => {
// Add your own code
},
className: "fa fa-star",
title: "Custom Button",
attributes: { // for custom attributes
id: "custom-id",
"data-value": "custom value" // HTML5 data-* attributes need to be enclosed in quotation marks ("") because of the dash (-) in its name.
}
},
"|" // Separator
// [, ...]
]
});
Put some buttons on dropdown menu
const easyMDE = new EasyMDE({
toolbar: [{
name: "heading",
action: EasyMDE.toggleHeadingSmaller,
className: "fa fa-header",
title: "Headers",
},
"|",
{
name: "others",
className: "fa fa-blind",
title: "others buttons",
children: [
{
name: "image",
action: EasyMDE.drawImage,
className: "fa fa-picture-o",
title: "Image",
},
{
name: "quote",
action: EasyMDE.toggleBlockquote,
className: "fa fa-percent",
title: "Quote",
},
{
name: "link",
action: EasyMDE.drawLink,
className: "fa fa-link",
title: "Link",
}
]
},
// [, ...]
]
});
EasyMDE comes with an array of predefined keyboard shortcuts, but they can be altered with a configuration option. The list of default ones is as follows:
Shortcut (Windows / Linux) | Shortcut (macOS) | Action |
---|---|---|
Ctrl-' | Cmd-' | "toggleBlockquote" |
Ctrl-B | Cmd-B | "toggleBold" |
Ctrl-E | Cmd-E | "cleanBlock" |
Ctrl-H | Cmd-H | "toggleHeadingSmaller" |
Ctrl-I | Cmd-I | "toggleItalic" |
Ctrl-K | Cmd-K | "drawLink" |
Ctrl-L | Cmd-L | "toggleUnorderedList" |
Ctrl-P | Cmd-P | "togglePreview" |
Ctrl-Alt-C | Cmd-Alt-C | "toggleCodeBlock" |
Ctrl-Alt-I | Cmd-Alt-I | "drawImage" |
Ctrl-Alt-L | Cmd-Alt-L | "toggleOrderedList" |
Shift-Ctrl-H | Shift-Cmd-H | "toggleHeadingBigger" |
F9 | F9 | "toggleSideBySide" |
F11 | F11 | "toggleFullScreen" |
Here is how you can change a few, while leaving others untouched:
const editor = new EasyMDE({
shortcuts: {
"toggleOrderedList": "Ctrl-Alt-K", // alter the shortcut for toggleOrderedList
"toggleCodeBlock": null, // unbind Ctrl-Alt-C
"drawTable": "Cmd-Alt-T", // bind Cmd-Alt-T to drawTable action, which doesn't come with a default shortcut
}
});
Shortcuts are automatically converted between platforms. If you define a shortcut as "Cmd-B", on PC that shortcut will be changed to "Ctrl-B". Conversely, a shortcut defined as "Ctrl-B" will become "Cmd-B" for Mac users.
The list of actions that can be bound is the same as the list of built-in actions available for toolbar buttons.
You can catch the following list of events: https://codemirror.net/doc/manual.html#events
const easyMDE = new EasyMDE();
easyMDE.codemirror.on("change", () => {
console.log(easyMDE.value());
});
You can revert to the initial text area by calling the toTextArea
method. Note that this clears up the autosave (if enabled) associated with it. The text area will retain any text from the destroyed EasyMDE instance.
const easyMDE = new EasyMDE();
// ...
easyMDE.toTextArea();
easyMDE = null;
If you need to remove registered event listeners (when the editor is not needed anymore), call easyMDE.cleanup()
.
The following self-explanatory methods may be of use while developing with EasyMDE.
const easyMDE = new EasyMDE();
easyMDE.isPreviewActive(); // returns boolean
easyMDE.isSideBySideActive(); // returns boolean
easyMDE.isFullscreenActive(); // returns boolean
easyMDE.clearAutosavedValue(); // no returned value
EasyMDE is a continuation of SimpleMDE.
SimpleMDE began as an improvement of lepture's Editor project, but has now taken on an identity of its own. It is bundled with CodeMirror and depends on Font Awesome.
CodeMirror is the backbone of the project and parses much of the Markdown syntax as it's being written. This allows us to add styles to the Markdown that's being written. Additionally, a toolbar and status bar have been added to the top and bottom, respectively. Previews are rendered by Marked using GitHub Flavored Markdown (GFM).
I originally made this fork to implement FontAwesome 5 compatibility into SimpleMDE. When that was done I submitted a pull request, which has not been accepted yet. This, and the project being inactive since May 2017, triggered me to make more changes and try to put new life into the project.
Changes include:
https://
by defaultMy intention is to continue development on this project, improving it and keeping it alive.
You may want to edit this library to adapt its behavior to your needs. This can be done in some quick steps:
gulp
command, which will generate files: dist/easymde.min.css
and dist/easymde.min.js
;Want to contribute to EasyMDE? Thank you! We have a contribution guide just for you!
Author: Ionaru
Source Code: https://github.com/Ionaru/easy-markdown-editor
License: MIT license
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My latest project at Flatiron was to use neural networks to classify satellite image tiles. I chose to use a convolutional neural network (CNN) and create a dataset of webscraped images to train the model with. This will just be a quick rundown of what went into the project with additional links to my articles to more of the technical parts. This way, it can help to familiarize you with the topics or help to share more about my work with those who have similar interests in computer vision and machine learning.
I chose to use a CNN because I read some school lessons on computer vision about how a CNN has advantages with image classification. A CNN uses pooling layers that filter through patches of the image pixels, finding common patterns, which develop into more complex patterns in order to help determine image class. I chose to work on a computer vision project with satellite images because there are possible use cases for solutions on Earth as well as use cases on other planets. I’ve read articles about organizations looking at different geological patterns on the Mars surface in search of the possible presence of water or perhaps its pre-existence on the planet. This led me to try and build the model to recognize river delta patterns here on Earth, with the next step being to train the model and locate delta patterns on Mars. The model could also eventually be useful for looking at changes to river deltas on Earth, for possible use in agriculture, climate change or even real estate. For now, the project is ongoing as of my writing this blog post, with training and testing performed on the Earth images. The Mars images will be the next part I’ll begin after graduation.
A land image tile.
A river delta image tile.
A Mars delta image tile.
In order to obtain the images for a dataset, I looked into some different API’s and webscraping with Beautiful Soup. Afterwards, I decided to use Selenium to scrape some images from an image search on Google. This method was able to scroll through the page interactively, which was necessary in order to have access to all of the images. I wrote a separate article about that process here. This method was useful as a starting point, in order to go through the process of building the dataset, creating the model, training, testing and just getting everything to work. The disadvantage was that there were a lot of images that were not clean, contained pieces of text or other image artifacts and overall led to less accurate results. There are some example images below so you can see what I mean. I do not claim copyright to any of the used images as they were used for an educational project for school and will remove them if anyone objects to their display on my article.
#convolutional-network #computer-vision #python #image-classification #machine-learning #neural networks
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A simple yet comprehensive approach to the concepts
Convolutional Neural Networks
Artificial intelligence has seen a tremendous growth over the last few years, The gap between machines and humans is slowly but steadily decreasing. One important difference between humans and machines is (or rather was!) with regards to human’s perception of images and sound.How do we train a machine to recognize images and sound as we do?
At this point we can ask ourselves a few questions!!!
How would the machines perceive images and sound ?
How would the machines be able to differentiate between different images for example say between a cat and a dog?
Can machines identify and differentiate between different human beings for example lets say differentiate a male from a female or identify Leonardo Di Caprio or Brad Pitt by just feeding their images to it?
Let’s attempt to find out!!!
The Colour coding system:
Lets get a basic idea of what the colour coding system for machines is
RGB decimal system: It is denoted as rgb(255, 0, 0). It consists of three channels representing RED , BLUE and GREEN respectively . RGB defines how much red, green or blue value you’d like to have displayed in a decimal value somewhere between 0, which is no representation of the color, and 255, the highest possible concentration of the color. So, in the example rgb(255, 0, 0), we’d get a very bright red. If we wanted all green, our RGB would be rgb(0, 255, 0). For a simple blue, it would be rgb(0, 0, 255).As we know all colours can be obtained as a combination of Red , Green and Blue , we can obtain the coding for any colour we want.
Gray scale: Gray scale consists of just 1 channel (0 to 255)with 0 representing black and 255 representing white. The colors in between represent the different shades of Gray.
Computers ‘see’ in a different way than we do. Their world consists of only numbers.
Every image can be represented as 2-dimensional arrays of numbers, known as pixels.
But the fact that they perceive images in a different way, doesn’t mean we can’t train them to recognize patterns, like we do. We just have to think of what an image is in a different way.
Now that we have a basic idea of how images can be represented , let us try and understand The architecture of a CNN
CNN architecture
Convolutional Neural Networks have a different architecture than regular Neural Networks. Regular Neural Networks transform an input by putting it through a series of hidden layers. Every layer is made up of a set of neurons, where each layer is fully connected to all neurons in the layer before. Finally, there is a last fully-connected layer — the output layer — that represent the predictions.
Convolutional Neural Networks are a bit different. First of all, the layers are organised in 3 dimensions: width, height and depth. Further, the neurons in one layer do not connect to all the neurons in the next layer but only to a small region of it. Lastly, the final output will be reduced to a single vector of probability scores, organized along the depth dimension
A typical CNN architecture
As can be seen above CNNs have two components:
In this part, the network will perform a series of **convolutions **and pooling operations during which the features are detected. If you had a picture of a tiger , this is the part where the network would recognize the stripes , 4 legs , 2 eyes , one nose , distinctive orange colour etc.
Here, the fully connected layers will serve as a classifier on top of these extracted features. They will assign a** probability** for the object on the image being what the algorithm predicts it is.
Before we proceed any further we need to understand what is “convolution”, we will come back to the architecture later:
What do we mean by the “convolution” in Convolutional Neural Networks?
Let us decode!!!
#convolutional-neural-net #convolution #computer-vision #neural networks
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Hello fellow people, It is instructive for instance to trace the computer industry’s to decline in vision, idealism, creativity, romance and sheer fun as it becomes more important and prosperous.
Here, Lets look into computational neural network architecture and constructing a cnn model for detection of ship using satellite imagery.
1. They are extensively used in computer vision problems. They differ from Multi-Layer Perceptron in manner and relatively cheap computing.
2. They are mainly used to classify, detect or recognize objects from image or video data.
Extract the features of the object present on the image by detecting specific patterns within the picture. The computer will scan a part of the image, usually with a matrix dimension known as Filter i.e.,3x3 matrix. The output of the convolution layer is called a Feature map.
Strides: The number of pixels shifts over the input matrix. When the stride is 1 then we move the filters to 1 pixel at a time. When the stride is 2 then we move the filters to 2 pixels at a time and so on.
3.Pooling Layer:
Pooling is done to reduce the dimensionality of the input image.
Eg: View the diagram. “pooling” will screen a 4x4 feature map and return the maximum value. The pooling takes the maximum value of a 2x2 array and then move this windows by two pixels.
4. Fully Connected Layer: Fully Connected layers in a neural networks are those layerswhere all the inputs from one layer are connected to every activation unit of the next layer.
5. Dense Layer or Output Layer: It takes the input and return the output using appropriate activation function.
#image-recognition #computer-vision #data-science #convolutional-network #neural network
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In this article, we will learn about how computers see images & the issues faced while performing a computer vision task. We will see how deep learning comes into the picture & how with the power of neural networks, we can build a powerful computer vision system capable of solving extraordinary problems.
One example of how deep learning is transforming computer vision is facial recognition or face detection. On the top left, you can see the icon of the human eye which visually represents vision coming into the deep neural network in the form of images, pixels, videos & on the output on the bottom you can see a depiction of the human face or detection of the human face or this could also be recognizing different human faces or emotions on the face and also the key facial features, etc.
#convolution-neural-net #computer-vision #neural-networks #cnn #convolutional-network #series