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Humans have the innate ability to identify the objects that they see in the world around them. The visual cortex present in our brain can distinguish between a cat and a dog effortlessly in almost no time. This is true not only with cats and dogs but with almost all the objects that we see. But a computer is not as smart as a human brain to be able to this on its own. Over the past few decades, Deep Learning researchers have tried to bridge this gap between human brain and computer through a special type of artificial neural networks called Convolutional Neural Networks(CNNs).
After a lot of research to study mammalian brains, researchers found that specific parts of the brain get activated to specific type of stimulus. For example, some parts in the visual cortex get activated when we see vertical edges, some when we see horizontal edges, and some others when we see specific shapes, colors, faces, etc. ML researchers imagined each of these parts as a layer of neural network and considered the idea that a large network of such layers could mimic the human brain.
This intuition gave rise to the advent of CNN, which is a type of neural network whose building blocks are convolutional layers. A convolution layer is nothing but a set of weight matrices called kernels or filters which are used for convolution operation on a feature matrix such as an image.Convolution:
2D convolution is a fairly simple operation, you start with a kernel and ‘stride’ (slide) it over the 2D input data, performing an element-wise multiplication with the part of the input it is currently on, and then summing up the results into a single output cell. The kernel repeats this process for every location it slides over, converting a 2D matrix of features into another 2D matrix of features.
The step size by which the kernel slides on the input feature matrix is called stride. In the below animation, the input matrix has been added with an extra stripe of zeros from all four sides to ensure that the output matrix is of the same size as the input matrix. This is called (zero)padding.
2D Convolution: kernel size=3x3, padding=1 or ‘same’, stride=1
Image segmentation is the task of partitioning a digital image into multiple segments (sets of pixels) based on some characteristics. The objective is to simplify or change the image into a representation that is more meaningful and easier to analyze.
Semantic Segmentation refers to assigning a class label to each pixel in the given image. See the below example.
Note that segmentation is different from classification. In classification, complete image is assigned a class label whereas in segmentation, each pixel in an image is classified into one of the classes.
Having a fair idea about convolutional networks and semantic image segmentation, let’s jump into the problem we need to solve.
Severstal is among the top 50 producers of steel in the world and Russia’s biggest player in efficient steel mining and production. One of the key products of Severstal is steel sheets. The production process of flat sheet steel is delicate. From heating and rolling, to drying and cutting, several machines touch flat steel by the time it’s ready to ship. To ensure quality in the production of steel sheets, today, Severstal uses images from high-frequency cameras to power a defect detection algorithm.
2.1. Mapping the business problem to an ML problemOur task is to
To put it together, it is a semantic image segmentation problem.2.2. Performance metricThe evaluation metric used is the mean Dice coefficient. The Dice coefficient can be used to compare the pixel-wise agreement between a predicted segmentation and its corresponding ground truth. The formula is given by:
where X is the predicted set of pixels and Y is the ground truth.
Read more about Dice Coefficient here.2.3. Data OverviewWe have been given a zip folder of size 2GB which contains the following:
**train_images**
—a folder containing 12,568 training images (.jpg files)**test_images**
— a folder containing 5506 test images (.jpg files). We need to detect and localize defects in these images**train.csv**
— training annotations which provide segments for defects belonging to ClassId = [1, 2, 3, 4]**sample_submission.csv**
— a sample submission file in the correct format, with each ImageId repeated 4 times, one for each of the 4 defect classes.More details about data have been discussed in the next section.
The first step in solving any machine learning problem should be a thorough study of the raw data. This gives a fair idea about what our approaches to solving the problem should be. Very often, it also helps us find some latent aspects of the data which might be useful to our models.
**_train.csv _**tells which type of defect is present at what pixel location in an image. It contains the following columns:
**ImageId**
: image file name with .jpg extension**ClassId**
: type/class of the defect, one of [1, 2, 3, 4]**EncodedPixels**
: represents the range of defective pixels in an image in the form of run-length encoded pixels(pixel number where defect starts pixel length of the defect).#convolutional-network #semantic-segmentation #computer-vision #dilated-convolution #deep-learning #deep learning
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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
1595436720
Humans have the innate ability to identify the objects that they see in the world around them. The visual cortex present in our brain can distinguish between a cat and a dog effortlessly in almost no time. This is true not only with cats and dogs but with almost all the objects that we see. But a computer is not as smart as a human brain to be able to this on its own. Over the past few decades, Deep Learning researchers have tried to bridge this gap between human brain and computer through a special type of artificial neural networks called Convolutional Neural Networks(CNNs).
After a lot of research to study mammalian brains, researchers found that specific parts of the brain get activated to specific type of stimulus. For example, some parts in the visual cortex get activated when we see vertical edges, some when we see horizontal edges, and some others when we see specific shapes, colors, faces, etc. ML researchers imagined each of these parts as a layer of neural network and considered the idea that a large network of such layers could mimic the human brain.
This intuition gave rise to the advent of CNN, which is a type of neural network whose building blocks are convolutional layers. A convolution layer is nothing but a set of weight matrices called kernels or filters which are used for convolution operation on a feature matrix such as an image.Convolution:
2D convolution is a fairly simple operation, you start with a kernel and ‘stride’ (slide) it over the 2D input data, performing an element-wise multiplication with the part of the input it is currently on, and then summing up the results into a single output cell. The kernel repeats this process for every location it slides over, converting a 2D matrix of features into another 2D matrix of features.
The step size by which the kernel slides on the input feature matrix is called stride. In the below animation, the input matrix has been added with an extra stripe of zeros from all four sides to ensure that the output matrix is of the same size as the input matrix. This is called (zero)padding.
2D Convolution: kernel size=3x3, padding=1 or ‘same’, stride=1
Image segmentation is the task of partitioning a digital image into multiple segments (sets of pixels) based on some characteristics. The objective is to simplify or change the image into a representation that is more meaningful and easier to analyze.
Semantic Segmentation refers to assigning a class label to each pixel in the given image. See the below example.
Note that segmentation is different from classification. In classification, complete image is assigned a class label whereas in segmentation, each pixel in an image is classified into one of the classes.
Having a fair idea about convolutional networks and semantic image segmentation, let’s jump into the problem we need to solve.
Severstal is among the top 50 producers of steel in the world and Russia’s biggest player in efficient steel mining and production. One of the key products of Severstal is steel sheets. The production process of flat sheet steel is delicate. From heating and rolling, to drying and cutting, several machines touch flat steel by the time it’s ready to ship. To ensure quality in the production of steel sheets, today, Severstal uses images from high-frequency cameras to power a defect detection algorithm.
2.1. Mapping the business problem to an ML problemOur task is to
To put it together, it is a semantic image segmentation problem.2.2. Performance metricThe evaluation metric used is the mean Dice coefficient. The Dice coefficient can be used to compare the pixel-wise agreement between a predicted segmentation and its corresponding ground truth. The formula is given by:
where X is the predicted set of pixels and Y is the ground truth.
Read more about Dice Coefficient here.2.3. Data OverviewWe have been given a zip folder of size 2GB which contains the following:
**train_images**
—a folder containing 12,568 training images (.jpg files)**test_images**
— a folder containing 5506 test images (.jpg files). We need to detect and localize defects in these images**train.csv**
— training annotations which provide segments for defects belonging to ClassId = [1, 2, 3, 4]**sample_submission.csv**
— a sample submission file in the correct format, with each ImageId repeated 4 times, one for each of the 4 defect classes.More details about data have been discussed in the next section.
The first step in solving any machine learning problem should be a thorough study of the raw data. This gives a fair idea about what our approaches to solving the problem should be. Very often, it also helps us find some latent aspects of the data which might be useful to our models.
**_train.csv _**tells which type of defect is present at what pixel location in an image. It contains the following columns:
**ImageId**
: image file name with .jpg extension**ClassId**
: type/class of the defect, one of [1, 2, 3, 4]**EncodedPixels**
: represents the range of defective pixels in an image in the form of run-length encoded pixels(pixel number where defect starts pixel length of the defect).#convolutional-network #semantic-segmentation #computer-vision #dilated-convolution #deep-learning #deep learning
1597469369
Crop and resize image size before upload in laravel using jquery copper js. In this post, i will show you how to crop and resize image size in laravel using jQuery copper js in laravel.
This laravel crop image before upload using cropper js looks like:
Laravel crop image before upload tutorial, follow the following steps and learn how to use cropper js to crop image before uploading in laravel app:
Read More => https://www.tutsmake.com/laravel-crop-image-before-upload-using-jquery-copper-js/
Live Demo Laravel Crop image Before Upload.
#laravel crop image before upload, #laravel crop and resize image using cropper.js #ajax image upload and crop with jquery and laravel #crop and upload image ajax jquery laravel #crop image while uploading with jquery laravel #image crop and upload using jquery with laravel ajax
1592373051
The aim of semantic image segmentation is to classify each pixel of an image. Image segmentation is a computer vision task which involves…
#convolutional-neural-net #machine-learning #semantic-segmentation #deep-learning #fully-convolutional-net #programming
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(https://analyticsindiamag.com/google-arts-culture-uses-ai-to-preserve-endangered-languages/)
Semantic Segmentation laid down the fundamental path to advanced Computer Vision tasks such as object detection, shape recognition, autonomous driving, robotics, and virtual reality. Semantic segmentation can be defined as the process of pixel-level image classification into two or more Object classes. It differs from image classification entirely, as the latter performs image-level classification. For instance, consider an image that consists mainly of a zebra, surrounded by grass fields, a tree and a flying bird. Image classification tells us that the image belongs to the ‘zebra’ class. It can not tell where the zebra is or what its size or pose is. But, semantic segmentation of that image may tell that there is a zebra, grass field, a bird and a tree in the given image (classifies parts of an image into separate classes). And it tells us which pixels in the image belong to which class.
In this article, we discuss semantic segmentation using TensorFlow Keras. Readers are expected to have a fundamental knowledge of deep learning, image classification and transfer learning. Nevertheless, the following articles might fulfil these prerequisites with a quick and clear understanding:
Let’s dive deeper into hands-on learning.
#developers corner #densenet #image classification #keras #object detection #object segmentation #pix2pix #segmentation #semantic segmentation #tensorflow #tensorflow 2.0 #unet