title: YOLO V3 Not Learning Well From Data
body: <p>I tried training YOLO V3 on a signature dataset, but the trained model after 2000 iterations couldn't produce any detection. </p>
  
  <p>The data set consisted of around 5000 images with signatures on them. The document pages are black and white. The images are labeled accurately as the pages are generated by me by placing signatures on the pages. </p>
  
  <p>I used the YOLOv3 default architecture, but trained from scratch. I tried using <code>darknet53.conv.74</code> and fine tuning it, but it didn't work, which I assume is because the network is trained on photo data while the data I have are documents. Training from scratch, I trained on a GPU AWS machine for 2000 iterations. During training, the output is like follows:</p>
  
  <p>It went from:</p>
  
  <p><code>2: 3249.269043, 3238.557373 avg, 0.000000 rate, 10.226817 seconds, 128 images Loaded: 0.000073 seconds</code></p>
  
  <p>To:</p>
  
  <p><code>2032: 0.667013, 0.644689 avg, 0.001000 rate, 22.906654 seconds, 130048 images Loaded: 0.000103 seconds</code></p>
  
  <p>So the training loss has significantly decreased and has been hovering around 0.6 for couple hundred iterations at least. </p>
  
  <p>The only part I'm not 100% sure is how to start the training process, and I used the code below to train it. </p>
  
  <pre><code>./darknet detector train params/darknet.data params/darknet-yolov3.cfg
  </code></pre>
  
  <p><code>darknet.data</code> is as follows:</p>
  
  <pre><code>classes = 1
  train  = ./params/data_train.txt
  valid  = ./params/data_test.txt
  names = ./params/classes.names
  backup = ./params/weights/
  </code></pre>
  
  <p>And <code>darknet-yolov3.cfg</code> is the exact same as <code>yolov3.cfg</code>. </p>
  
  <p>I tried testing the model using couple different images with signatures on them, and they are rather simple cases. But the trained model failed to detect any signature in all of these test images. </p>
  
  <p>If anyone has any suggestions on what I should do to/test, it would be greatly appreciated! Thanks!</p>