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

Description
In some rare cases, for example, when you need to finetune a large model on a small dataset the majoring part of training loop is waiting for saving model checkpoints to a hard drive.

Proposal
Would be logically to add a CheckpointCallback with parameter save_n_best=0 to a configuration and do not store best checkpoints and instead use the latest state of the model.

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

We need to create a separate dependencies list for react usage. Many react users won't need youtube-dl or ffmpeg libaries and we don't want things to be super bloated if they're using it as an npm module.

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.

during CI on my hiwenet, I discovered a ton of deprecation warnings from scipy : https://travis-ci.org/github/raamana/hiwenet/jobs/658810855

mostly asking medpy to move from scipy to numpy for functions such as sum, absolute, sqrt, square etc .. see below for a sample, and the above link for a full list.. If you can, can you try fix them? thanks.

  /hom