
English: 
[MUSIC PLAYING]
This is an example of
a synthetic dataset
that is publicly available.
So you might recognize
the game from the photo.
So it's pretty-- not perfect,
but pretty photo-realistic.
And the name of that dataset
is "Playing for a Benchmark."
And you can use that
type of synthetic images
in order to train your network.
And the idea is
that this way, you
can get a lot of driving
scenario without having
to go outside with a real car.
And more importantly, since
you control everything,
you know everything in
your synthetic dataset.
You know where are
the object, where
are your pedestrian, et cetera.

Chinese: 
[音乐]
这是一个公开的
合成数据集示例。
你可能可以看出照片中的游戏。
所以很简单——不完美，
但是图片反映现实。
该数据集的名称为
“Playing for a Benchmark”。
你可以用那一类的合成图像
训练自己的网络。
我们的想法是，如此这般，
你便可以获得很多驾驶场景，
而无需驾驶真正的车。
更重要的是，因为你控制一切，
便会了解你的
合成数据集中的一切。
你知道物品在哪里，
人行道在哪里，诸如此类。

English: 
So you get the data
notation for free.
And this is a very costly path
if you have to do it manually.
So having it for free is
really a great benefit.
So there is other dataset
that are available.
So one can use CARLA, which
is an open-source game
engine that you can use to
generate your driving scenario.
It's more basic, but
pretty interesting too.
And there is another
publicly available dataset
called Synscapes,
which is probably
the most photo-realistic,
but a bit restricted
in terms of diversity.
So the question is,
is it a good idea
to use synthetic images in order
to train the network that will
be deployed in the real world?
So we did a couple
of experiments.
And the first question
is, how do we train?
[MUSIC PLAYING]

Chinese: 
这样你便能免费获得数据标记。
如果需要亲力亲为，
必将耗资巨大。
所以，能免费获得是
一个很大的益处。
所以还有其他可用的数据集。
我们可用使用CARLA，
这是一个开源游戏
引擎，你可以用它生成
自己的驾驶场景。
它更为基础，
但是也很有趣。
还有一个开放的数据集，
名为Synscapes，
它可能算得上是
最具有照片真实感，
但在多样性方面有限制的
数据集了。
问题来了，用合成照片
训练将部署在现实世界的网络
是一个好主意吗？
我们做了几个实验。
第一个问题是：
我们如何训练？
[音乐]
