Cityscape | Generates simple geometric cityscapes in the style

Use align-self-end on the column...

After some time, I did find a solution for this problem. An important thing to know is that, by default, train_crop_size and vis_crop_size are 513x513.

The issue was due to vis_crop_size being smaller than the input images, so vis_crop_size is needed to be greater than the max dimension of the biggest image.

In case you want to use export_model.py, you must use the same logic than vis.py, so your masks are not cropped to 513 by default.

Your formulas are not correct. You can't take the mean of the values of a batch and then the standard deviation of these means and expect it to be the standard deviation over the entire dataset. Try something like:

Call .cuda() on the model during initialization.

As per your above comments, you have GPUs, as well as CUDA installed, so there's no point of checking the device availability with torch.cuda.is_available().

Additionally, you should wrap your model in nn.DataParallel to allow PyTorch use every GPU you expose it to. You also could do DistributedDataParallel, but DataParallel is easier to grasp initially.

Example initialization:

It has nothing to do with python. In general,

Tensorflow is a graph based mathematical framework that abstracts for you all of those complex vectorial or matricial operations you face, particularly in machine learning.

What the developers though is that it would be unconfortable to specify every single time how many input vectors you need to pass in your model for the training, so they decided to abstract it for you.

You will not interested if your model is fed with one single or thousands samples as long as the output matches with the input dimension (but also any internal operation should match in dimensions!).

We need a placeholder because (None, 2) is a different shape with respect of just (2,), because in the first case we know we will face 2 dimensions.

Even if the None dimension is unknown when you "compile" your model, it will be evaluated only when it is strictly needed, in other words when you run it. In this way your model will be happy to run on a batch size of 64 as like as 128 samples.

For the rest a (non-scalar) Tensor behaves like a normal numpy array:

problem was the tensorflow version , do the following: uninstall current version

UNet is absent from the benchmark so i assume it is not adapted for this dataset (too slow and not enough performant probably). However, I advise you to start with DeepLabv3+ from Google which is not so complicated and more adapted for this dataset.

You can use this repository where it is implemented, well documented and useable with pretrained weights from cityscape dataset (and also PascalVOC dataset).

• When albumId and filters are used, an error of The album ID cannot be set if filters are used. occurs. So when you want to use filters, please remove albumId.
• The value of includedContentCategories is an array as follows.
  • "includedContentCategories": ["LANDSCAPES","CITYSCAPES"]
• includeArchiveMedia is includeArchivedMedia.
• Please include includeArchivedMedia in filters.

When above points are reflected to your script, it becomes as follows.