<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Keras | Sadra Naddaf</title><link>https://www.sadra.dev/tag/keras/</link><atom:link href="https://www.sadra.dev/tag/keras/index.xml" rel="self" type="application/rss+xml"/><description>Keras</description><generator>Source Themes Academic (https://sourcethemes.com/academic/)</generator><language>en-us</language><copyright>© 2024 Sadra Naddaf - All Rights Reserved</copyright><lastBuildDate>Sun, 12 Jul 2020 23:31:12 -0500</lastBuildDate><image><url>https://www.sadra.dev/images/icon_hu816e2f5c59e09e1eb1152837f78b95eb_24768_512x512_fill_lanczos_center_2.png</url><title>Keras</title><link>https://www.sadra.dev/tag/keras/</link></image><item><title>Coral Image Segmentation</title><link>https://www.sadra.dev/project/coral_detect/</link><pubDate>Sun, 12 Jul 2020 23:31:12 -0500</pubDate><guid>https://www.sadra.dev/project/coral_detect/</guid><description>&lt;p>During the Spring of 2020, I utilized deep learning segmentation to do coral segmentation. I used a dataset of ~ 150 images of corals, which contains eight different categories.&lt;/p>
&lt;ul>
&lt;li>Pre-processed images using PIL and OpenCV and annotated. I used different techniques, including but not limited to, histogram equalization, white balance, color balance.&lt;/li>
&lt;li>I have done data augmentation because of the small size of the dataset. Methods used: cropping, rotation, mirror and etc.&lt;/li>
&lt;li>Dataset annotated carefully using labelme.&lt;/li>
&lt;li>Used different ResNet, U-Net, FCN, Vanilla CNN, and etc.&lt;/li>
&lt;li>Hyperparameter tuning.&lt;/li>
&lt;li>Segmented images of 8-different categories of corals with the accuracy of mIoU 60.1 %, considering the size of the dataset and the time I had, It&amp;rsquo;s a good one.&lt;/li>
&lt;/ul></description></item></channel></rss>