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<!DOCTYPE html>
<html>
<head>
<meta charset='utf-8'>
<meta http-equiv="X-UA-Compatible" content="chrome=1">
<meta name="description" content="TffRCNN resnet50 : Faster rcnn based on tensorflow and resnet50">
<link rel="stylesheet" type="text/css" media="screen" href="stylesheets/stylesheet.css">
<title>TffRCNN resnet50</title>
</head>
<body>
<!-- HEADER -->
<div id="header_wrap" class="outer">
<header class="inner">
<a id="forkme_banner" href="https://github.com/miraclebiu/TFFRCN_resnet50">View on GitHub</a>
<h1 id="project_title">TffRCNN resnet50</h1>
<h2 id="project_tagline">Faster rcnn based on tensorflow and resnet50</h2>
<section id="downloads">
<a class="zip_download_link" href="https://github.com/miraclebiu/TFFRCN_resnet50/zipball/master">Download this project as a .zip file</a>
<a class="tar_download_link" href="https://github.com/miraclebiu/TFFRCN_resnet50/tarball/master">Download this project as a tar.gz file</a>
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<section id="main_content" class="inner">
<h1>
<a id="tffrcn_resnet50" class="anchor" href="#tffrcn_resnet50" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>TFFRCN_Resnet50</h1>
<p>This is the faster rcnn based on Tensorflow and resnet50</p>
<ul>
<li>fork from the TFFRCN website is <a href="https://github.com/CharlesShang/TFFRCNN">https://github.com/CharlesShang/TFFRCNN</a>
</li>
<li>just change the python file factory.py ， __ init __.py ，networks.py in /lib/networks </li>
<li>add resnet50 networks </li>
<li>resnet101 hasn't been tested</li>
</ul>
<p>You can find how to use it from the above website</p>
<p>the model file is convert from the caffemodel, you can download from the baiduyun<br>
link: <a href="http://pan.baidu.com/s/1eSuUO1s">http://pan.baidu.com/s/1eSuUO1s</a> pwd: 24cf<br>
the fine-tune model link: <a href="http://pan.baidu.com/s/1nuUYfMh">http://pan.baidu.com/s/1nuUYfMh</a> pwd：5bve<br>
resnet101 imagenet file link <a href="http://pan.baidu.com/s/1i5odxNv">http://pan.baidu.com/s/1i5odxNv</a> pwd:d8y4 </p>
<p>USing voc07_trainval to train the Resnet50 and test on the voc07_test<br>
The result is :
Mean AP = 0.7124</p>
<p>AP for aeroplane = 0.7801<br>
AP for bicycle = 0.7931<br>
AP for bird = 0.6836<br>
AP for boat = 0.5750<br>
AP for bottle = 0.4892<br>
AP for bus = 0.8322<br>
AP for car = 0.8412<br>
AP for cat = 0.8380<br>
AP for chair = 0.5186<br>
AP for cow = 0.7550<br>
AP for diningtable = 0.6369<br>
AP for dog = 0.7861<br>
AP for horse = 0.7966<br>
AP for motorbike = 0.7692<br>
AP for person = 0.7765<br>
AP for pottedplant = 0.4454<br>
AP for sheep = 0.7131<br>
AP for sofa = 0.7074<br>
AP for train = 0.8150<br>
AP for tvmonitor = 0.6953 </p>
</section>
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<footer class="inner">
<p class="copyright">TffRCNN resnet50 maintained by <a href="https://github.com/miraclebiu">miraclebiu</a></p>
<p>Published with <a href="https://pages.github.com">GitHub Pages</a></p>
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