From f856f6e7f1fd0a11244527b266c1fd47334fb5f3 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sun, 16 Sep 2018 01:54:09 +0000
Subject: [PATCH] 10-class model for card detection

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 README.md |   14 +++++++++++++-
 1 files changed, 13 insertions(+), 1 deletions(-)

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 ## Sept 14th, 2018
 --------------------
 
-Thankfully, OpenCV had an implementation for DNN, which supports YOLO as well. They have done quite an amazing job, and the speed isn't too bad, either. I can score about 20~25fps on my tiny YOLO, without using GPU.
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+Thankfully, OpenCV had an implementation for DNN, which supports YOLO as well. They have done quite an amazing job, and the speed isn't too bad, either. I can score about 20~25fps on my tiny YOLO, without using GPU.
+
+
+## Sept 15th, 2018
+--------------------
+
+I tried to do an alternate approach - instead of making model identify cards as annonymous, train the model for EVERY single card. As you may imagine, this isn't sustainable for 10000+ different cards that exists in MTG, but I thought it would be reasonable for classifying 10 different cards.
+
+Result? Suprisingly effective.
+
+<img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_1.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_decision_result_2.jpg" width="360"><img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_detection_result_3.jpg" width="360"> <img src="https://github.com/hj3yoo/darknet/blob/master/figures/4_decision_result_4.jpg" width="360">
+
+They're of course slightly worse than annonymous detection (notice how I used white background, too) and impractical for any large number of cardbase, but it was an interesting approach.
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