title: How to learn using my dataset on deeplab v3 plus
body: <p>In deeplab v3p, although I trained my data sets, it did not work.
  Why is it?</p>
  
  <p><strong>My environment is the bellow:</strong></p>
  
  <pre><code>OS Platform and Distribution: Ubuntu 16.04 lts
  TensorFlow installed from: conda
  TensorFlow version: 18
  Bazel version: no
  CUDA/cuDNN version: cuda 9 cudnn 7
  GPU model and memory: titan x (2 cards), 64gb memory
  Exact command to reproduce: -
  </code></pre>
  
  <p>I did the following work:</p>
  
  <p><strong>a) generate simple dataset:</strong></p>
  
  <p>The training image arranges two figures (circle and rect) randomly and draws color randomly.
  The label image is set to 0RGB(0, 0, 0) for the background, 1 RGB (1,1,1) for the rect, 2RGB(2, 2, 2) for the circle and 255 RGB (255, 255, 255) for the line as the segmentation.</p>
  
  <p><a href="https://camo.qiitausercontent.com/2be21942628a85c46fd8ffb99e05b257b4b0c980/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f62653262306638632d333933322d363434322d336132342d6534306333613466653865612e706e67" rel="nofollow noreferrer">https://camo.qiitausercontent.com/2be21942628a85c46fd8ffb99e05b257b4b0c980/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f62653262306638632d333933322d363434322d336132342d6534306333613466653865612e706e67</a></p>
  
  <p><a href="https://camo.qiitausercontent.com/323d4f21d792138ebb52f994dc42cc0b5d3ab30d/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f34656633633261332d633533372d316134632d653633342d3165663632643734623166392e706e67" rel="nofollow noreferrer">https://camo.qiitausercontent.com/323d4f21d792138ebb52f994dc42cc0b5d3ab30d/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f34656633633261332d633533372d316134632d653633342d3165663632643734623166392e706e67</a></p>
  
  <p><strong>b) convert to tf.record</strong></p>
  
  <p>In the <code>build_voc2012_data.py</code>, I changed file path from origin datasets to my datasets.
  Then, I ran it and created tf.record.</p>
  
  <p><strong>c) training</strong></p>
  
  <p>In the <code>segmentation_dataset.py</code>, I added DATASETS INFORMATION.</p>
  
  <pre><code>_MYDATASETS_INFORMATION = DatasetDescriptor(
      splits_to_sizes={
          'train': 10000,
          'trainval': 1000,
          'val': 1000,
      },
      num_classes=3,
      ignore_label=255,
  )
  
  
   _DATASETS_INFORMATION = {
       'cityscapes': _CITYSCAPES_INFORMATION,
       'pascal_voc_seg': _PASCAL_VOC_SEG_INFORMATION,
       'ade20k': _ADE20K_INFORMATION,
       'mydata': _MYDATASETS_INFORMATION
   }
  </code></pre>
  
  <p>Then, I started training.</p>
  
  <pre><code>python train.py   --logtostderr   --train_split=trainval   --model_variant=xception_65   --atrous_rates=6   --atrous_rates=12   --atrous_rates=18   --output_stride=16   --decoder_output_stride=4   --train_crop_size=513   --train_crop_size=513   --train_batch_size=4   --training_number_of_steps=1000000   --fine_tune_batch_norm=true   --tf_initial_checkpoint="./datasets/y/model.ckpt"  --train_logdir="./datasets/x"  --dataset_dir="./datasets/z/tfrecord" --dataset=mydata
  </code></pre>
  
  <p><strong>d) finished training</strong></p>
  
  <p>As a result of one million iterations, the minimum loss was 4.765.
  When it started trainning, it was 7.56.</p>
  
  <p><strong>e) VIS.</strong>
  two of the test images.</p>
  
  <p>input image: <a href="https://camo.qiitausercontent.com/e37afb4ae0e5232a0c0828dad779a4a852462a45/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f64363930326231622d386164302d306366612d656433642d6436346537383462636134322e706e67" rel="nofollow noreferrer">https://camo.qiitausercontent.com/e37afb4ae0e5232a0c0828dad779a4a852462a45/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f64363930326231622d386164302d306366612d656433642d6436346537383462636134322e706e67</a></p>
  
  <p>output image:
  <a href="https://camo.qiitausercontent.com/d638a2fc7fe5f5119e2f00d7c0aa59b9b8fc73d4/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f36643963643265372d373234362d636434612d303566322d6335623266396237623865352e706e67" rel="nofollow noreferrer">https://camo.qiitausercontent.com/d638a2fc7fe5f5119e2f00d7c0aa59b9b8fc73d4/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f36643963643265372d373234362d636434612d303566322d6335623266396237623865352e706e67</a></p>
  
  <p>input image: <a href="https://camo.qiitausercontent.com/8fe89aa43aac3a322ede1ac46d469fc48da6b809/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f64313162643834372d323734372d643530642d393366642d6231353661353137613934632e706e67" rel="nofollow noreferrer">https://camo.qiitausercontent.com/8fe89aa43aac3a322ede1ac46d469fc48da6b809/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f64313162643834372d323734372d643530642d393366642d6231353661353137613934632e706e67</a></p>
  
  <p>output image:
  <a href="https://camo.qiitausercontent.com/94f15de43ce6195bdc8602b800d1275a4830193c/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f63623061666663652d613566302d363333302d306362382d6261393964363735333734652e706e67" rel="nofollow noreferrer">https://camo.qiitausercontent.com/94f15de43ce6195bdc8602b800d1275a4830193c/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e616d617a6f6e6177732e636f6d2f302f39353633362f63623061666663652d613566302d363333302d306362382d6261393964363735333734652e706e67</a></p>
  
  <p>From the above results, I think that Deeplab v3 does not work well.
  Is my usage wrong? Or is the parameter setting incorrect?
  Or is the learning not enough?</p>