Add augmentation in usage/init.md
@@ -15,13 +15,13 @@ This is an example of configuration.
choose task type classification
choose network LmnetV1Quantize
choose dataset format Caltech101
- enable data augmentation? No
training dataset path: {dataset_dir}/train/
set validataion dataset? (if answer no, the dataset will be separated for training and validation by 9:1 ratio.) yes
validataion dataset path: {dataset_dir}/test/
batch size (integer): 64
image size (integer x integer): 32x32
how many epochs do you run training (integer): 100
+ enable data augmentation? No
apply quantization at the first layer: yes
```
@@ -29,6 +29,23 @@ This is an example of configuration.
You don't need to re-run `bluoil init` again.
`blueoil init` just generates a config file in YAML format. You can change some settings, according to comments.
-#### How to get details about data augmentation?
+#### data augmentation
+You can use various augmentation methods in generated YAML, also you can change augmentation methods's parameter.
+Under `commmon.data_augmentation` in generated yaml, augmentation methods are listed and the parameters are nested in each methods.
+
+
+generated yaml:
+```
+common:
+ data_augmentation:
+ - Blur:
+ - value: (0, 1)
+ - Color:
+ - value: (0.75, 1.25)
+ - Contrast:
+ - value: (0.75, 1.25)
+ - FlipLeftRight:
+ - probability: 0.5
+```
-Please see <a href="../reference/data_augmentor.html">here</a> for reference.
+Please see <a href="../reference/data_augmentor.html">the data augmentor reference page</a>, when you want to know about augmentation methods. the all of augmentation methods and parameter are explained, methods name in generated yaml correspond to class name under `lmnet.data_augmentor` in the reference.
