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tasks.html
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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>ICPR CONTEST</title>
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<style>
body {
background-color : rgb(250,250,250); /*UCLA bgcolor*/
}
</style>
</head>
<body>
<div class="container">
<div class="jumbotron">
<div class="content">
<h1 style="text-align:center; margin-top:80px; font-weight: bold; color:rgb(100,10,250); font-size: 48px;">
Object Detection in Aerial Images (ODAI)
</h1>
<!-- <p style="text-align:center; color:blue; position:relative; top:7%"> -->
<p style="text-align:center; margin-top:15px; color: rgb(10,10,250); font-size: 32px;">
<strong>
<span style="font-style:italic">A Contest on ICPR'2018 </span>
</strong>
</p>
<!-- <body background="images/largeimage2.jpg"></body> -->
<!-- <img src="images/largeimage2.jpg" class="img-responsive center-block" /> -->
</div>
</div>
<div class="row">
<div class="span12">
<div class="row">
<div class="span4">
</div>
<div class="span4">
<ul class="nav nav-tabs">
<!-- <li class="active"> -->
<li >
<a href="index.html">Home</a>
</li>
<li>
<a href="dataset.html">Dataset</a>
</li>
<li class="active">
<!-- <li> -->
<a href="tasks.html">Tasks</a>
</li>
<li>
<a href="evaluation.html">Evaluation</a>
</li>
<li>
<a href="results.html">Results</a>
</li>
<br />
</ul>
</div>
<div class="span4">
</div>
</div>
<h2>
Overview
</h2>
<p>
We propose two detection tasks. Task1 uses the initial annotation as ground truth.
Task2 uses the generated axis-aligned bounding boxes as ground truth.
The results from task2 are of great practical value.
We recommond you to test your algorithms by way of Task1.
You can use the provided train/val data to train and validate your detector. Validation data may also be used
for training when submitting results on the test set.
External datas of any form is allowed. But must be reported during submission. Fine-tuning models that are pretrained
on ImageNet or COCO are also allowed.
</p>
<h2>Task1 - Detection with oriented bounding boxes <strong>(Recommended)</strong> </h2>
<p>
The aim of this task is to locate the ground object instances with an oriented bounding box. The
oriented bounding box follows the same format with the original annotation
</p>
<!-- <h3>
Evaluation Server
</h3>
<p>
For evaluation, you must registrate and submit on the
<a href="http://www.icdar2017chinese.site:5080/evaluation1/">Evaluation Server</a>
</p> -->
<h3>
Submission Format
</h3>
<p>
You will be asked to submit a zip file containing results for all test images to evaluate your results. The results are stored
in 15 files, <strong style="color:blue">"Task1_plane.txt, Task1_storage-tank.txt, ..."</strong>, each file contains all the results for a specific category.
Each file is in the following format:
</p>
<pre>
<code style="font-size:16px">
imgname score x<sub>1</sub> y<sub>1</sub> x<sub>2</sub> y<sub>2</sub> x<sub>3</sub> y<sub>3</sub> x<sub>4</sub> y<sub>4</sub>
imgname score x<sub>1</sub> y<sub>1</sub> x<sub>2</sub> y<sub>2</sub> x<sub>3</sub> y<sub>3</sub> x<sub>4</sub> y<sub>4</sub>
...
</code>
</pre>
<a href="submissionformat/example_task1.rar">An example submission of task1</a>
<h3>
Evaluation Protocol
</h3>
<p>
The evaluation protocol for oriented bounding box is a little different from the protocol in the
original PASCAL VOC. We use the intersection over the union area of two polygons(ground truth
and prediction) to calculate the IoU. The rest follows the PASCAL VOC.
</p>
<h2>
Task2 - Detection with horizontal bounding boxes
</h2>
<p>
Detecting object with horizontal bounding boxes is usual in many previous contests for object
detection. The aim of this task is to accurately localize the instance in terms of horizontal bounding
box with (x, y, w, h) format. In the task, the ground truths for training and testing are generated
by calculating the axis-aligned bounding boxes over original annotated bounding boxes.
</p>
<p>
</p>
<!-- <h3>
Evaluation Server
</h3>
<p>
For evaluation, you must registrate and submit on the
<a href="http://www.icdar2017chinese.site:5080/evaluation1/">Evaluation Server</a>
</p> -->
<h3>
Submission Format
</h3>
<p>
You will be asked to submit a zip file containing results for all test images to evaluate your results. The results are stored
in 15 files, <strong style="color:blue">"Task2_plane.txt, Task2_storage-tank.txt, ..."</strong>, each file contains all the results for a specific category.
</p>
<pre>
<code style="font-size:16px">
imgname score x y w h
imgname score x y w h
...
</code>
</pre>
<a href="submissionformat/example_task2.rar">An example submission of task2</a>
<h3>
Evaluation Protocol
</h3>
<p>
The evaluation protocol for horizontal bounding boxes follows the PASCAL VOC benchmark,
which uses mean Average Precision(<strong>mAP</strong>) as the primary metric.
</p>
</div>
</div>
<br>
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