act version

src/main/python/deeplearn/README.md

@@ -1,5 +1,6 @@
 based on https://github.com/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow
-#Summary
+
+# Summary
 - files in / data (images jpeg, masks png)
 - preprocess_data.py start: conversion to 512x512 and rotations
 - dataset / generate_dataset.py -> create filelists
@@ -12,19 +13,19 @@ based on https://github.com/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow
 With the help of this implementation objects can be segmented.
 
 ## 1. Requirements
-`` `
+```
 Python 3.5
 Tensorflow 1.4.0
 Jupyter notebook
 NVIDIA graphics card (optional for GPU based training)
-`` `
+```
 
 ## 2. Preparations
 Run the * setup.sh * script to create all necessary folders
 and download the Resnet Checkfile.
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+```
 bash setup.sh
-`` `
+```
 
 ## 3. Training
 Store all data for the training in the directory * data *. Pictures are in the
@@ -38,21 +39,21 @@ Update: use preprocess_data.py -> adjust file separator on split
 ### 3.1 Creating a dataset
 For the training now a dataset must be created. For this the script call
 * generate_dataset_txt.py * in the * dataset * folder. Text files are created with file lists for testing, training and validation.
-`` `
+```
 python generate_dataset_txt.py
-`` `
+```
 
 ### 3.2 Start training
 Then the training can be started. Simply call the script
 * main.py *.
 
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+```
 python main.py
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+```
 
 If you like, the hyperparameters can be adjusted in the * main.py * script. Important is the number of steps * num_steps * and * save_interval *. Watch for a falling loss when running the workout.
 
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+```
 num_steps: how many iterations to train
 
 save_interval: how many steps to save the model and save a checkpoint
@@ -64,29 +65,29 @@ weight_decay: l2 regularization parameter
 learning_rate: initial learning rate
 
 power: parameter for poly learning rate
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+```
 
 ### 3.3 Tensor board for visualization
 During the training you can visualize every checkpoint. In the
 Main directory execute the following command.
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+```
 tensorboard --logdir = log
-`` `
+```
 
 ## 4. Testing / Validation
 To test and validate please first adjust the parameters in the * Main.py *.
 The checkpoint number and the number of validations (number of
 Lines in the file * valid.txt * in the directory * data_preprocessed *)
 
-`` `
+```
 valid_step: checkpoint number for testing / validation
 
 valid_num_steps: = number of testing / validation samples
-`` `
+```
 Then call the script * main.py * with the parameter test as follows.
-`` `
+```
 python main.py --option = test
-`` `
+```
 You then get the "pixel accuracy" and the "mean IoU". In the future also the
 F1 score.