myDrive | Node.js and mongoDB Google Drive Clone | Runtime Evironment library

You can try using tf.py_function to integrate PIL operations in graph mode. Here is an example with a batch size of 1 to keep it simple (you can change the batch size afterwards):

Before

It's difficult to give a concrete answer without looking at the full annotation file, but a KeyError exception is raised when trying to access a key that is not in a dictionary. From the error message you've posted, this key seems to be 'segmentation'.

This is not in your code snippet, but before even getting into network training, have you done any exploration/inspections using the registered datasets? Doing some basic exploration or inspections would expose any problems with your dataset so you can fix them early in your development process (as opposed to letting the trainer catch them, in which case the error messages could get long and confounding).

In any case, for your specific issue, you can take the registered training dataset and check if all annotations have the 'segmentation' field. A simple code snippet to do this below.

See this tutorial. You should be able to create two datasets (train and test) based on the clients and their data:

You might want to check out this answer to a related question.

It contains the implementation of a workaround based on the following observation (from another answer in the same thread):

Note: If you are using this in some type of script that is creating new files/folders and quickly reading the 'user.drive.id' afterwards, be aware that it can take many seconds for the "real" file id to be generated. If you read the value of 'user.drive.id' and it starts with 'local', this means that it has not yet generated an actual file id. In my opinion, the best way to deal with this is to create an asynchronous loop that sleeps between checks, and then returns the file id once it no longer starts with 'local'.

ps. I would rather post this as a comment, but I don't have enough rep points yet ;-)

As far as I understood you. You want just to clean non-arabic chars (so chars like 1 @ ? gonna not be deleted).

If you want another char to be deleted just add it to charsotdelete.

If you have any questions let me know.

You can keep a dictionary of sizes and delete any images that have a size that have already been seen. That way you don't need a nested loop, and don't have to create Image objects for the same file multiple times.

The problem is the default shape used for the VGG19 model. You can try replacing the input shape and applying a Flatten layer just before the output layer. Here is a working example:

Well a few things. For training set you say you have 16,0000 images. However with a batch size of 32 and steps_per_epoch= 100 then for any given epoch you are only training on 3,200 images. Similarly you have 2000 validation images, but with a batch size of 32 and validation_steps = 5 you are only validating on 5 X 32 = 160 images. Now Vgg is an OK model but I don't use it because it is very large which increases the training time significantly and there are other models out there for transfer learning that are smaller and even more accurate. I suggest you try using EfficientNetB3. Use the code

It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason