In file binary segmentation (camvid).ipynb, block 5, there is:

# Lets look at data we have
dataset = Dataset(x_train_dir, y_train_dir, classes=['car', 'pedestrian'])
image, mask = dataset[5] # get some sample
visualize(
    image=image, 
    cars_mask=mask[..., 0].squeeze(),
    sky_mask=mask[..., 1].squeeze(),
    background_mask=mask[..., 2].squeeze(),
)

here, sky_mask

Is your feature request related to a problem? Please describe.
The 1.3 branch breaks physics-based network object and sync functionality.

Describe the solution you'd like
Add option to sync Rigidbody including axis restrictions, preferably.

Additional context
1.2 screenshots:
![options](https://user-images.githubusercontent.com/623830/50857796-aeacf400-135c-11e9-9252-569d2b7

1. 31页，第一行
   
   应改成
   

2. 35 页， 正定矩阵的定义 （公式 2-15）
   标准正定矩阵的定义，应该没有 “对称矩阵” 这一条件吧？ 只是您在本书大多讨论的是“对称”正定矩阵，所以加上了 “对称” 这一条件？

3. 38页， 奇异值分解定义（公式 2-16）
   ![default](https://

如果设置
cfg.NUM_TRAINERS = 4
cfg.TRAINER_ID = 0，1，2，3
if self.shuffle and cfg.NUM_TRAINERS > 1: np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines) num_lines = len(self.all_lines) // cfg.NUM_TRAINERS self.lines = self.all_lines[num_lines * cfg.TRAINER_ID: num_lines * (cfg.TRAINER_ID + 1)] self.shuffle_seed += 1
上面代码中的self.

Is there a corresponding paper?

This article states: "We will present our solution at the IEEE International Conference on Data Science and Advanced Analytics (DSAA)". Unfortunately, I couldn't find the corresponding paper.

Bests,
Sebastian