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

Hi,

I try to understand Deepdetect right now, starting with the Plattforms Docker container.
It looks great on pictures, but I have a hard time right now using it :)

My Problem: The docs seems to step over important points, like using JupyterLab. All examples shows the finished Custom masks, but how do I get them?

Is there something missing in the docs?

Example: https://www.deepdetec

(I will compile a list and hopefully open a PR if needed)

Describe the bug
This behavior is present in a plethora of catalyst's callbacks and losses. It's consistent, but it's definitely confusing for many new users.

To Reproduce
Steps to reproduce the behavior:
Use these functions/classes:
Callbacks

• [MeterMetricsCallback](https://github.com/catalyst-team/catalyst/blob/mas

We have a lot of repetitive instances in our data and it a simple Copy/Cut - Paste (Ctrl+C/X and Ctrl+V) function for bounding boxes would help speed up labelling by a lot!

Edit: Also multi-selection of multiple boxes/geometries would be great for a faster workflow

Keras-rcnn was written to be compatible with a number of third-party frameworks and services like Apple’s Core ML framework that enables developers to embed Keras models into their iOS applications. We should document how an Apple developer can create, train, and export their model to their Core ML-compatible iOS application.

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.