Merge branch 'master' of https://github.com/hellochick/ICNet-tensorflow

README.md

@@ -3,6 +3,9 @@
   This is an implementation of ICNet in TensorFlow for semantic segmentation on the [cityscapes](https://www.cityscapes-dataset.com/) dataset. We first convert weight from [Original Code](https://github.com/hszhao/ICNet) by using [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow) framework.
 
 ## Update
+#### 2017/11/15:
+1. Support `training phase`, you can train on your own dataset. Please read the guide below.
+
 #### 2017/11/13:
 1. Add `bnnomerge model` which reparing for training phase. Choose different model using flag `--model=train, train_bn, trainval, trainval_bn` (Upload model in google drive).
 2. Change `tf.nn.batch_normalization` to `tf.layers.batch_normalization`.
@@ -26,6 +29,7 @@ List of Args:
 --model=trainval    - To select trainval_90k model
 --model=train_bn    - To select train_30k_bn model
 --model=trainval_bn - To select trainval_90k_bn model
+--model=others      - To select your own checkpoint
 ```
 Inference time:  ~0.02s, I have no idea why it's faster than caffe implementation 
 
@@ -57,6 +61,50 @@ Input image                |  Output image
 :-------------------------:|:-------------------------:
 ![](https://github.com/hellochick/ICNet_tensorflow/blob/master/input/test_1024x2048.png)  |  ![](https://github.com/hellochick/ICNet_tensorflow/blob/master/output/test_1024x2048.png)
 
+## Training on your own dataset
+> Note: This implementation is different from the details descibed in ICNet paper, since I did not re-produce model compression part. Instead, we train on the half kernel directly.
+
+### Step by Step
+**1. Change the `DATA_LIST_PATH`** in line 22, make sure the list contains the absolute path of your data files, in `list.txt`:
+```
+/ABSOLUTE/PATH/TO/image /ABSOLUTE/PATH/TO/label
+```
+**2. Set Hyperparameters** (line 21-35) in `train.py`
+```
+BATCH_SIZE = 48
+IGNORE_LABEL = 0
+INPUT_SIZE = '480,480'
+LEARNING_RATE = 1e-3
+MOMENTUM = 0.9
+NUM_CLASSES = 27
+NUM_STEPS = 60001
+POWER = 0.9
+RANDOM_SEED = 1234
+WEIGHT_DECAY = 0.0001
+```
+Also **set the loss function weight** (line 38-40) descibed in the paper:
+```
+# Loss Function = LAMBDA1 * sub4_loss + LAMBDA2 * sub24_loss + LAMBDA3 * sub124_loss
+LAMBDA1 = 0.16
+LAMBDA2 = 0.4
+LAMBDA3 = 1.0
+```
+**3.** Run following command and **decide whether to update mean/var or train beta/gamma variable**. Remember to choose `--model=others`.
+```
+python train.py --update-mean-var --train-beta-gamma --model=others
+```
+After training the dataset, you can run following command to get the result:  
+```
+python inference.py --img-path=YOUR_OWN_IMAGE --model=others
+```
+### Result ( inference with my own data )
+
+Input                      |  Output
+:-------------------------:|:-------------------------:
+![](https://github.com/hellochick/ICNet_tensorflow/blob/master/input/indoor1.jpg)  |  ![](https://github.com/hellochick/ICNet-tensorflow/blob/master/output/indoor1.jpg)
+![](https://github.com/hellochick/ICNet_tensorflow/blob/master/input/indoor3.jpg)  |  ![](https://github.com/hellochick/ICNet-tensorflow/blob/master/output/indoor3.jpg)
+
+
 ## Citation
     @article{zhao2017icnet,
       author = {Hengshuang Zhao and