Yolo what does it mean

Что такое YOLO. Объясняем простыми словами

YOLO (от англ. you only live once — «ты живешь только один раз») — понятие, которое описывает стремление исполнять желания и получать удовольствие прямо сейчас, не отказываясь от этого ради будущего благополучия.

Это понятие раскрывает альтернативный подход к образу жизни. Мы не откладываем все наши сбережения на «светлое будущее», отказывая себе во всём, а живём здесь и сейчас. Ситуация с пандемией наглядно показала, что будущее может быть не совсем светлым и тех возможностей, которые могут быть сейчас, в дальнейшем просто может не быть. Поэтому YOLO-мышление отразилось на всех сферах жизни, в том числе и на экономике.

Во-первых, у людей стало меняться отношение к работе: удалёнка, размывание рабочего времени и обязанностей, а также эмоциональное выгорание всё больше побуждают людей увольняться со стабильной работы в крупных корпорациях, а накопленные за время локдауна средства позволяют рискнуть и заняться тем, чем давно хотелось, например начать собственный маленький бизнес. Люди хотят больше времени посвящать себе и своей семье и видеть смысл в своей работе, даже если это приносит меньший доход.

YOLO-эффект на рынке труда ещё больше склоняет чашу весов в сторону гиг-экономики, фриланса, уберизации, аутстаффинга, аутсорсинга и гибридизации рабочих отношений.

Пример употребления на «Секрете»

«В пандемию люди уже начали задавать себе вопросы: а стоит ли эта работа моих сил и времени? Так, на Западе появилось целое понятие — YOLO-экономика. Это состояние, при котором люди стремятся максимально улучшить условия своей жизни, даже если ради этого придётся рискнуть стабильной работой. Ситуация с курьерами ещё больше толкает на изменения».

(CEO Solar Staff Павел Шинкаренко — о выросших зарплатах курьеров.)

История

Аббревиатура YOLO приобрела популярность в 2011-м после сингла рэпера Дрейка «The Motto». В 2012-м Оксфордский американский словарь включил этот сленговый термин в шорт-лист на «Английское слово года».

Крупные корпорации уже озабочены оттоком сотрудников и начали принимать меры. К примеру, LinkedIn дал сотрудникам оплачиваемую неделю отдыха, Houlihan Lokey оплатил отдых сотрудников в отпуске, Twitter предоставил лишний выходной в месяц, «Сбер» разрешил брать саббатикал на 12 месяцев с сохранением места работы.

The Real Meaning of YOLO

The Real Meaning of YOLO

It is the acronym for “ You Only Live Once”. Due to the ever-increasing popularity of the catchphrase, many have misunderstand the meaning behind it. To many, this acronym have become an excuse to act rashly or do something really dumb. It has become the common response to most of the questions that demands why something have to done for youths.

So what YOLO really means? It is not about making rash or stupid choices when you are drunk or being a complete bozo. YOLO has a deeper meaning behind it. Similar to carpe diem, which implies “seize the day” in Latin. YOLO is a phrase to constantly remind us, that you only live once and we have to make the best out of the days we have.

It means to seize the day, to make your life extraordinary, to chase your dreams, to live your life to the fullest and sometimes even doing something you don’t usually do so that you have a story to tell for the rest of your life.

It will not be wise to use YOLO for everything you do. It is okay to let loose, have some fun and even being a complete bozo at times. But understand that YOLO was never an excuse to commit silly decisions.

Life is short, if you turn around you might miss it. So take YOLO as a reminder to live an extraordinary life.

Here’s the Meaning of ‘YOLO’ for Those Who Have No Idea

One of the latest trendy acronyms kids are using online

YOLO is a popular online acronym that stands for:

You Only Live Once

That’s right—this is an acronym that is meant to remind you of your own mortality and force you to ask yourself, am I going to make the most of it?

How YOLO Is Used

YOLO is used as a motto to convey the idea that you should take risks and live life to the fullest because you only have one life to live and you may be missing out on a lot of exciting things.

It can be used as a standalone acronym or in a sentence. Some take it further by using it as an adjective. See the following examples to get a better understanding of how this acronym is typically used.

Examples of YOLO in Use

Example 1

Friend #1: «You weren’t in class this morning. Where were you?»

Friend #2: «Skipped it to go go-carting because YOLO»

In this first example, Friend #2 uses YOLO to justify their behavior. They want to make it seem like they’re living life to the fullest, which, according to them, involves skipping class sometimes.

Example 2

Friend #1: «I just drove two hours all the way home after messing up the suspension on my car from driving over some train tracks»

Friend #2: «That is so YOLO of you»

In this second example, Friend #2 uses YOLO as if it’s an adjective. They’re so impressed by Friend’s number one daring behavior that was probably very unsafe.

Example 3

Facebook status update: «Didn’t check FB for a whole day. YOLO»

This last example shows how YOLO might be used in a sarcastic sense. Not checking Facebook isn’t typically considered a thrilling, risky or potentially life-threatening activity, but this Facebook poster uses it to make it seem like it is.

How ‘YOLO’ Started

Although the full phrase, you only live once has been used casually for years, the acronym exploded to become a huge trend in pop culture largely thanks to Canadian music artist Drake, who featured the acronym in his hip-hop single, The Motto. On October 23rd of 2011 and according to Know Your Meme, Drake sent out a tweet with YOLO in it.

The Viral Spread of YOLO

Sometimes all it takes is a simple post from an influential figure or celebrity to set off a new trend, which was clearly the case with YOLO. A significant increase in Twitter activity with tweets including YOLO as a keyword or hashtag had taken place on October 24th—just a mere day after it was tweeted by Drake.

Today, there isn’t a social network in existence that probably hasn’t had the YOLO acronym shared on its platform. Social media users on Facebook, Twitter, Instagram, Tumblr, and other social networks now commonly use the #YOLO hashtag to post about their once-in-a-lifetime ideas.

Some people are serious about it and others use it as a joke. The humor and tendency to exaggerate the acronym has helped contribute to the trend’s spread across the social web.

Here are a few places you can look to see publicly posted #YOLO content:

Several web enthusiasts have taken to using meme generator tools to create and share images that promote the popular YOLO trend. Meme Center has a collection of user-generated YOLO memes that you can browse through.

Parodies of YOLO

YOLO went viral because social media users knew how to take its use to new and ridiculous heights. While some people legitimately used it to describe risky or daring experiences, like traveling alone to a foreign country, or deciding against a traditional wedding and planning to elope, other users took it as an opportunity to use the acronym to describe even the most mundane experiences.

Typing YOLO after a relatable, everyday experience is a popular way to use the acronym. Social media users seemed to find quite a lot of amusement in coming up with posts like, «Woke up at 10:13 a.m. #YOLO,» or «Pet my cat for a full five minutes today. #YOLO.»

For the sake of web humor, anything can be a YOLO experience. These parodies are the ones you’ll often see shared online these days and made into memes.

A Different Interpretation of YOLO

In the midst of all the YOLOing, some social media users decided to dive deeper into the meaning behind the phrase. While everyone believed it was something to say to encourage people to take more risks and be fearless, other social media users began pointing out that YOLO actually means the exact opposite.

They argue that since YOLO implies you only have one life to live, you should take care of yourself by being careful and always planning ahead when taking risks. Rather than carelessly throwing yourself out into risky situations without giving any thought to it first, you should do everything you can to stay safe.

And so, it turns out that YOLO really has two different definitions, depending on how you personally decide to interpret it. You can now find YOLO in Oxford Dictionaries.

What Does Yolo Mean? The Definition, Consequences, And Lifestyle

What Yolo means. You hear and see it everywhere, whether on a forum or as a graffiti tag on the wall. When you see people in the area doing the craziest things, they shout ‘YOLO.’ But what is the meaning of YOLO? Some explain that it is a lifestyle; others see it more as an internet slang cry such as SWAG or LMAO.

The truth, however, is that YOLO started living a separate life as a word, thus demonstrating a new movement and vision of experience within the youth generation. You only live once!

The meaning of YOLO as a slogan:

Y ou O nly L ive O nce

Literally translated, it means: you only live once. The slogan mainly implies that when people doubt a risky activity: doing something crazy, something dangerous or shameful, people remember that they only live once and that they can, therefore, actually make everything.

You often hear the cry in combination with a word related to indifference, such as: ‘care’ and ‘buoys.’ An example is that someone is challenged to empty a glass of vodka in one go, who thinks about it, a friend shouts: “buoys, YOLO!” The term is so powerful and inciting that the boy drinks the glass.

From internet slang to daily spoken language

In response to the word YOLO, you can indicate that today words or expressions are created on the internet and that the same terms trickle down into the daily spoken language. Think of expressions such as ‘YOLO’ and ‘SWAG’ but also ‘LOL’, a fully integrated word in society. “LOL” is only an abbreviation for the term “laughing out loud.” Nowadays, these expressions mainly come from sites such as 4chan or 9gag, where many people spread cries and take over through funny pictures.

A good example is a quote from the movie “Lord of the rings” with a picture of a character who says: “one does not simply ….” + a funny and original addition. The comic effect of this is the repetition and the fact that only the people who are members of the circle understand it.

This expression also seeps through to the daily language use of young people, and the use itself is a form to identify yourself with people with the same language and, therefore, also the same humor. A group is created in which young people use the same internet slang that other people do not understand.

The YOLO lifestyle

The rise of the YOLO cry has created a new lifestyle. Many young people start living irresponsibly or risky with the motto: you only live once, and you have to make the most of it. For example, some people see it as a positive motivation to go on great journeys or finally address the girl from their dreams. On the other hand, there are people who, because of the YOLO institution, drink that glass of vodka too much or go into bed with the first one.

This way, you can see that many interpretations are possible within the term. The agreement is that you do risky, adventurous things that you would not normally do so quickly. The lifestyle is at odds with a bourgeois ‘safe’ lifestyle and can thus be described as revolutionary. Nowadays young people want to ‘live,’ experience,

The YOLO paradox

There is, however, a principal contradiction within the YOLO lifestyle. If it is so important that people live only once and therefore take as many risks as possible, they increase the chance of ending one life soon. One could also connect YOLO to the value of life: you only live once, be careful with experience. However, at the moment, it is mainly an excuse to do the craziest and irresponsible things.

Often it causes funny situations, but sometimes things go completely wrong with rapper Ervin McKinness, he tweeted YOLO before he got into his car drunk and died in an accident. This indicates once again that one must be careful with such revolutionary irresponsible lifestyles.

YOLO Explained

YOLO or You Only Look Once, is a popular real-time object detection algorithm. YOLO combines what was once a multi-step process, using a single neural network to perform both classification and prediction of bounding boxes for detected objects. As such, it is heavily optimized for detection performance and can run much faster than running two separate neural networks to detect and classify objects separately. It does this by repurposing traditional image classifiers to be used for the regression task of identifying bounding boxes for objects. This article will only look at YOLOv1, the first of the many iterations this architecture has gone through. Although the subsequent iterations feature numerous improvements, the basic idea behind the architecture stays the same. YOLOv1 referred to as just YOLO, can perform faster than real-time object detection at 45 frames per second, making it a great choice for applications that require real-time detection. It looks at the entire image at once, and only once — hence the name You Only Look Once — which allows it to capture the context of detected objects. This halves the number of false-positive detections it makes over R-CNNs which look at different parts of the image separately. Additionally, YOLO can generalize the representations of various objects, making it more applicable to a variety of new environments. Now that we have a general overview of YOLO, let’s take a look at how it really works.

YOLO is based on the idea of segmenting an image into smaller images. The image is split into a square grid of dimensions S×S, like so:

The cell in which the center of an object, for instance, the center of the dog, resides, is the cell responsible for detecting that object. Each cell will predict B bounding boxes and a confidence score for each box. The default for this architecture is for the model to predict two bounding boxes. The classification score will be from `0.0` to `1.0`, with`0.0` being the lowest confidence level and `1.0` being the highest; if no object exists in that cell, the confidence scores should be `0.0`, and if the model is completely certain of its prediction, the score should be `1.0`. These confidence levels capture the model’s certainty that there exists an object in that cell and that the bounding box is accurate. Each of these bounding boxes is made up of 5 numbers: the x position, the y position, the width, the height, and the confidence. The coordinates `(x, y)` represent the location of the center of the predicted bounding box, and the width and height are fractions relative to the entire image size. The confidence represents the IOU between the predicted bounding box and the actual bounding box, referred to as the ground truth box. The IOU stands for Intersection Over Union and is the area of the intersection of the predicted and ground truth boxes divided by the area of the union of the same predicted and ground truth boxes.

In addition to outputting bounding boxes and confidence scores, each cell predicts the class of the object. This class prediction is represented by a one-hot vector length C, the number of classes in the dataset. However, it is important to note that while each cell may predict any number of bounding boxes and confidence scores for those boxes, it only predicts one class. This is a limitation of the YOLO algorithm itself, and if there are multiple objects of different classes in one grid cell, the algorithm will fail to classify both correctly. Thus, each prediction from a grid cell will be of shape C + B * 5, where C is the number of classes and B is the number of predicted bounding boxes. B is multiplied by 5 here because it includes (x, y, w, h, confidence) for each box. Because there are S × S grid cells in each image, the overall prediction of the model is a tensor of shape S × S × (C + B ∗ 5 ).

Here is an example of the output of the model when only predicting a single bounding box per cell. In this image, the dog’s true center is represented by the cyan circle labeled ‘object center’; as such, the grid cell responsible for detecting and bounding the box is the one containing the cyan dot, highlighted in dark blue. The bounding box that the cell predicts is made up of 4 elements. The red dot represents the center of the bounding box, (x, y), and the width and height are represented by the orange and yellow markers respectively. It is important to note that the model predicts the center of the bounding box with widths and heights rather than top left and bottom right corner positions. The classification is represented by a one-hot, and in this trivial example, there are 7 different classes. The 5th class is the prediction and we can see that the model is quite certain of its prediction. Keep in mind that this is merely an example to show the kind of output that is possible and so the values may not be accurate to any real values. Below is another image of all the bounding boxes and class predictions that would actually be made and their final result.

YOLO Architecture

The YOLO model is made up of three key components: the head, neck, and backbone. The backbone is the part of the network made up of convolutional layers to detect key features of an image and process them. The backbone is first trained on a classification dataset, such as ImageNet, and typically trained at a lower resolution than the final detection model, as detection requires finer details than classification. The neck uses the features from the convolution layers in the backbone with fully connected layers to make predictions on probabilities and bounding box coordinates. The head is the final output layer of the network which can be interchanged with other layers with the same input shape for transfer learning. As discussed earlier, the head is an S × S × (C + B ∗ 5 ) tensor and is 7 × 7 × 30 in the original YOLO research paper with a split size S of 7, 20 classes C, and 2 predicted bounding boxes B. These three portions of the model work together to first extract key visual features from the image then classify and bound them.

YOLO Training

As discussed previously, the backbone of the model is pre-trained on an image classification dataset. The original paper used the ImageNet 1000-class competition dataset and pre-trained 20 out of the 24 convolution layers followed by an average-pooling and fully connected layer. They then add 4 more convolutions to the model as well as 2 fully connected layers as it has been shown that adding both convulsions and fully connected layers increases performance. They also increased the resolution from 244 × 244 to 448 × 448 pixels as detection requires finer details. The final layer, which predicts both class probabilities and bounding box coordinates, uses a linear activation function while the other layers use a leaky ReLU function. The original paper trained for 135 epochs on the Pascal VOC 2007 and 2012 datasets using a batch size of 64. Data augmentation and dropout were used to prevent overfitting, with a dropout layer with a rate of 0.5, used between the first and second fully connected layers to encourage them to learn different things (preventing co-adaptation). There are more details available on the learning rate scheduling and other training hyperparameters in the original paper.

The loss function is the simple squared sum, but it must be modified. Without modification, the model will weight localization error, the difference between predicted and true bounding box coordinates, and class prediction error the same. Additionally, when a grid cell doesn’t contain an object, its confidence score tends towards 0 which can overpower the gradients from other cells that do contain objects. Both issues are solved by using two coefficients, λcoord and λnoobj, which multiply the loss for the coordinates and the object losses respectively. These are set to λcoord = 5 and λnoobj = 0.5, increasing the weight of detection and decreasing the importance of no object loss. Finally, to weight small bounding box equality as much as large boxes, the width and height difference is square-rooted rather than used directly. This makes sure that the error is treated the same as in large and small boxes, which would otherwise discourage the model from predicting large boxes. For example, if the predicted width of the bounding box is 10 and the actual width is 8, and we use this equation

we find the loss is 4. When we scale up to a predicted width of 100 and an actual of 98, the loss is 4 again. However, a difference of 2 out of the true 98 is negligible compared to a difference of 2 out of 8. Therefore, the loss between 10 and 8 should be much larger than the loss between 100 and 98. So we use this equation instead:

Using this new equation, the loss for 10 and 8 is 0.111 while the loss for 100 and 98 is 0.010. Keep in mind that looking at loss as a number by itself is meaningless, but the difference between values is meaningful. So the fact that 0.111 is much smaller than 4 doesn’t matter, but what does matter is that the difference between loss for the large and small widths is 0% for the squared difference while the difference is 90.99% for the squared rooted difference. This example shows why the square root is important: we want to treat big and small bounding boxes the same.

Each grid cell predicts multiple bounding boxes, but only one bounding box is responsible for detecting the object. The responsible bounding box is determined by choosing the predicted bounding box with the highest IOU. The effect of this is that certain bounding boxes will improve at predicting certain shapes and sizes of bounding boxes while others will specialize in other shapes. This occurs because of the following: if there is a large object when the multiple bounding boxes predict bounds, the best one is rewarded and continues to improve predicting large boxes. When a small object comes up, the previous predictor fails at predicting a good fit as its bounding box is too large. However, another predictor has a better prediction, and it is rewarded for bounding the small object well. As training goes on, the predictions of various bounding boxes diverge to specialize in the tasks they were good at early in training time.

Let’s look at the loss function:

Let’s break down the math.

The double summation merely means to sum across all of the grid cells (an S × S square) and all of the bounding boxes B.