<h3>Project&nbsp;<a href="https://ods.ai/projects/lacmus">Lacmus</a></h3>

Every person going outdoors has a chance to get lost. Usually people get lost in the forest and wilderness. About 120 000 Persons are lost In Russia every year. More than 100 000 Persons are lost in the USA every year.

Usually a person, who is lost in wilderness, meets a number of risk factors, such as: Dehydration, Hypothermia, Trauma, Panic. Unfortunately, this person has a high chance of dying.

Various search and rescue teams around the world are looking for missing people. For searches, they often use unmanned aerial vehicles with which they perform aerial photography.

But... Analyzing photos also needs human resources. How can we speed up this process? We can use Neural Networks!

Our project, Lacmus, helps Search &amp; Rescue Squads to find people, who get lost, by analyzing photos from copters with Neural Networks. And now we are ready for The First Competition!

At the competition, we offer you to solve the problem of recognizing and detecting lost people in aerial images. It is a classical one class object detection task. We need to detect people in photos from copters with an estimated altitude about 50m (164 ft).

<img alt="" src="https://storage.yandexcloud.net/datasouls-ods/images/2021/07/01/346cf7dc-6335-4046-8564-d058081a5efb/data-example.jpeg" />

<h3>Task Definition</h3>

During the competition, you are invited to solve the&nbsp;one class object detection&nbsp;task.

<h3>Metrics</h3>

We considering two metrics:

<ul>
	<li><a href="https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173" rel="nofollow">mAP @ IoU 50</a></li>
	<li><a href="https://en.wikipedia.org/wiki/F-score" rel="nofollow">F1 @ IoU 50</a></li>
</ul>

The implementation of these metrics can be found in the file&nbsp;<a href="https://github.com/lacmus-foundation/events/blob/master/cv-competition-1/metrics/metrics.py"><code>metrics.py</code></a>

<h3>Submit Format</h3>

The submission has&nbsp;<code>csv</code>&nbsp;format the following fields:&nbsp;<code>id,xmin,ymin,xmax,ymax,score</code>. Where:

<ul>
	<li>id - id of image (e.g.&nbsp;<code>1.jpg</code>&nbsp;has id 1,&nbsp;<code>2.jpg</code>&nbsp;has id 2 etc);</li>
	<li>xmin, ymin, xmax, ymax - bounding box coordinates;</li>
	<li>score - prediction confidence (float: [0..1])</li>
</ul>

The submission file must include a header.

Example:

<pre lang="csv">
<code class="language-python">id,xmin,ymin,xmax,ymax,score
0,127,2877,271,2921,0.589
0,1751,2111,1888,2222,0.111
1,583,1111,666,1211,0.799
...</code></pre>

ODS slack channel&nbsp;<a href="https://opendatascience.slack.com/archives/CJ6UPQ3FZ">#ml4sg</a>

Every person has a chance to get lost. Usually people get lost in the forest or wilderness. At the competition, we offer you to solve the problem of recognizing and detecting lost people in aerial images.

Public

Baseline

Lacmus Foundation

Lacmus is a cross-platform software, which realize human detection on photos from unmanned aerial vehicle (UAV).

We are made automation of task to search the humans on set of photos from UAV.

We analyze photos from UAV using Neural Networks, Deep Learning &amp; Computer Vision to find people, who are lost in the wild forest.

This technology helps Search &amp; Rescue Services to find lost people in short time.

Increasing chances to resque a lost person.

Lacmus app should be installed on drone operator&#39;s notebooks to process a set of photos, downloaded from UAV after flight above people Search &amp; Rescue region.

Also we are working about building pipeline, which will help us to enlarge our dataset by using a number of photos from different Search &amp; Rescue drone operators from different regions.

Habr article [RU]:

&nbsp;<a href="https://habr.com/ru/company/ods/blog/483616/">https://habr.com/ru/company/ods/blog/483616/</a>

Datafest-2019 speech [RU]:

&nbsp;<a href="https://youtu.be/RQrjbAUoPNk?t=6270">https://youtu.be/RQrjbAUoPNk?t=6270</a>

Github:

&nbsp;<a href="https://github.com/lacmus-foundation/lacmus">https://github.com/lacmus-foundation/lacmus</a>

Telegram &nbsp;(Easy Way to Join Us) :

<a href="https://t.me/found_lacmus">https://t.me/found_lacmus</a>

We are waiting for ML researchers, backend/frontend developers and others with expertize in Computer Vision &amp; Deep Learning

&nbsp;

Members of our foundation:

gosha20777, kseniia, ei-grad, balezz, sergey_vakhreev, stasy, appetito, wanteich, Rumata, Palladdiumm, Silkindm, sergsh, sharov_am, nvsit, zvasil

e.t.c

<figure class="image"><img alt="https://raw.githubusercontent.com/lacmus-foundation/lacmus/master/docs/imgs/examples/04.png" src="https://raw.githubusercontent.com/lacmus-foundation/lacmus/master/docs/imgs/examples/04.png" />
<figcaption>BBoxed people on photo from UAV</figcaption>
</figure>

The open-source project engaged in the search & rescue for missing peo

ML for Social Good

Gorgy Perevozchikov

Palladdiumm

Andrew Grigorev

AndrejLabintsev

Sharov Anton

Yuriy Homyakov

Jilly Ush

Kseniia Terekhova

Sergey Gordeev

Lacmus

User Agreement

Privacy Policy

Consent

I give my {consent} to «Sorevnovaniya Analiza Dannykh» LLC to process and transfer my personal data (name, email, address) to the partners of the event for information interaction

first_name

First name

last_name

Last name

country

Country

city

City

email

Email

slack_name

Your ODS Slack nickname

study_place

Study place

study_specialty

Study specialty

work_place

Work place

work_position

Work position

expectations

Expectations

interests

Interests

preferred_language

Preferred language

about

About

Registration application for Data Fest & ODS Summer Of Code

1st Lacmus Computer Vision Competition

Predictions

Соревнование

Многоклассовая сегментация морского льда на спутниковых снимках

Зима близко, а значит, скоро в Арктике снова встанет лед (или нет). В любом случае, тема морского арктического льда сейчас как никогда актуальна - как с экологической, так и с бизнес точки зрения.

Хакатон

NewsViz ODS SoC

Мы будем оценивать контрибушены в проект NewsViz. За каждый вид контрибьюшенов в репозиторий будем начислять очки. Чтобы их зачесть, надо будет сабмитить ссылку на Issue или Pull Request. Количество очков будем определять по схеме ниже.

NewsVis ODS SoC

Теория

Как из одной инициативы ML4SG вырос маленький стартап

Проект NewsViz и его перспективы

<h3>Присоединяйтесь к ML4SG проектам на летней школе:</h3>

<ol>
	<li>Проект <a href="https://ods.ai/projects/lacmus">LACMUS</a> помогает Liza-Alert искать потерявшихся в лесу людей с помощью дронов. Помочь в этом <a href="http://ods.ai/tracks/ml4sg-df2021/competitions/lacmus-cvc-soc2021">нелегком деле</a>&nbsp;этим летом можете и вы.</li>
	<li>Ребята из <a href="https://ods.ai/projects/maritimeai">MaritimeAI</a>&nbsp;занимаются ML проектами с морскими данными. <a href="https://ods.ai/tracks/ml4sg-df2021/competitions/MaritimeAI-hack-soc2021">Распознавание морского льда</a>&nbsp;на космических снимках - отличная отдушина в такое жаркое и знойное лето.</li>
	<li>Проект <a href="https://ods.ai/projects/news-viz">NewsVis</a>&nbsp;анализируют и визуализируют большие потоки новостей. На летней школе <a href="https://ods.ai/tracks/ml4sg-df2021/competitions/newsvis-soc2021">проекту</a>&nbsp;пригодится любая помощь - от написания документации до разработки и рисерча нового функционала.&nbsp;</li>
</ol>

Эти 3 проекта участвуют в призовой программе <a href="https://ods.ai/tracks/summer-of-code-2021">ODS Summer of Code #1</a>. 
По ссылкам можно узнать больше про <a href="https://ods.ai/tracks/summer-of-code-2021/blocks/ae0e78c2-ae3e-468d-bc80-9346bab55465">призы</a>, а также&nbsp;про&nbsp;<a href="https://ods.ai/tracks/summer-of-code-2021/blocks/1c2fc4dc-1596-47b4-b56b-ab89f5a5b0b2">логистику и расписание</a>.

&nbsp;

Хотите узнать больше про ML4SG?

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Страница проекта: <a href="https://ods.ai/projects/ml4sg">https://ods.ai/projects/ml4sg</a>&nbsp;

Канал в Slack ODS.ai:&nbsp;<a href="https://opendatascience.slack.com/archives/CJ6UPQ3FZ">#ml4sg</a>

Трек проектов, в которых с помощью ML и других приблуд в области DS можно сделать мир лучше. Здесь мы поделимся новостями существующих проектов и будем очень рады новым участникам со свежими идеями!

Добро пожаловать на главное Data Science событие сообщества и нашу первую летнюю школу ODS!
Жми "подать заявку" и получи доступ ко всем трекам, активностям школы и нетворкинг комнатам в SpatilChat ODS.

#	Teams (12)	MAP Score	F1 Score
1	Sergey Zlobin	0.975934059	0.8335832084
2	Slava Dodatko	0.967593858	0.9778565102
3	Vadym Kozlovskyi	0.966741276	0.9778565102
4	Maxim Lyuzin	0.9614938751	0.9139515455
5	Yauhen Ustsinovich	0.9576977076	0.9181434599
6	Vasiliy Adeshkin	0.9175074573	0.5641558442
7	Alina Shchukina	0.8444422829	0.8578901482
8	Maria Potapova	0.0001027071	0.0053835801
9	Egor Pustovit	0.0001027071	0.0053835801
10	Alexey Voytsekhovskiy	0.0001027071	0.0053835801
11	Dmitrii Kutsenko	0.0001027071	0.0053835801
12	Мария Филиппова	0.0001027071	0.0053835801