Vision | sample Android app will be the demonstration | Android library

Vision uses images (hence the name ;-) ). If you don't want to use images, you need to use the Core ML API directly.

If the output from the first model really is an image, it's easiest to change that model's output type to an image so that you get a CVPixelBuffer instead of an MLMultiArray. Then you can directly pass this CVPixelBuffer into the next model using Vision.

You are confusing a few things here (I think)

You freeze the layer if you don't want them to be trained (and don't want them to be part of the graph also).

Usually we freeze part of the network creating features, in your case it would be everything up to self.head.

After that, we usually only train bottleneck (self.head in this case) to fine-tune it for the task at hand.

In case of your model it would be:

Following is an example how to create a grayscale image representing classes for a segmentation task or similar.

On some black background, draw some shapes with fill values in the range of 1, ..., #classes. For visualization purposes, this mask is plotted as perceived as a regular grayscale image as well as scaled to the said value range – to emphasize that the mask looks all black in general, but there's actual content in it. This mask is saved as a lossless PNG image, and then opened using Pillow, and converted to mode P. Last step is to set up a proper palette for the desired number of colors, and apply that palette using Image.putpalette.

If you just executed the code snippet you included in your question, you are not passing the value uri to the code properly. You need to parse the arguments you are passing on your python command. You can do this by adding argparse.

You can add output to this code. The count variable is needed to sequentially fill the array with integers from 0 and so on.

bins=250 creates 251 equally spaced bin edges between the lowest and highest values. These don't align well with the discrete values. When the difference between highest and lowest is larger than 250, some bins will be empty. When the difference is smaller than 250, some bins will get the values for two adjacent numbers, creating a spike. Also, when superimposing histograms, it is handy that all histograms use exactly the same bin edges.

You need the bins to be exactly between the integer values, setting bins=np.arange(-0.5, 256, 1) would achieve such. Alternatively, you can use seaborn's histplot(...., discrete=True).

Here is some code with smaller numbers to illustrate what's happening.

assuming your urlpatterns looks like this: path('external/', views.external, name='ext_data')

I'm not sure what "when I break the lines" means here. It doesn't appear that there is a SQL injection attack here. It looks like the JSON passed in the request has an enrollStatus of "2000 and select sleep(10)))); --" rather than, presumably, "2000". The query that is reported in the error message is using bind variables so this is just Oracle saying "hey, I'm comparing this thing you're passing in against a numeric value but I can't seem to convert it to a number". The actual value you're passing in doesn't appear in the query so it's not a SQL injection attack.

Now, from a security standpoint, the error message is leaking a whole lot of information about the underlying schema which I'd be uncomfortable handing an attacker. It is possible that knowing that information would help an attacker to craft a SQL injection attack somewhere else in the system or to access data they're not authorized to access.

The intuition of using OPTIONAL is correct. You have to add it for every single information that you want to consider optional (i.e. not necessary).

Furthermore, for avoiding false positives, I think you should also use rdfs:label instead of a generic ?label (which can refer to any property).