IoU | A C implement of intersection over union ratio

My labels were given wrong. I figured this out by trying to plot my dataset image with its labels and I figured out that it either wasn't showing the labels or not showing it accurately.

This evaluation function is based on COCO metric. It evaluates labels of all sizes so it is showing -1.000 for area=large. My current guess that it is because my dataset doesn't have varying sizes of labels. They are all of equal sizes and they are medium/small in size. I might be wrong.

Wrapping the transform_targets_for_output method with a try-except-raise clause and applying tf.data.experimental.ignore_errors to the dataset seems to actually work:

The standalone expression syntax is "create expression". Since I don't see anything else I presume you want to do a standalone expression and so its missing "create". And when its not a standalone expression the syntax is "expression ... select ...".

Here is a link to one of the regression tests.

model(img) will always return some kinds of results, even if there are no objects detected. What you need to do is inspect the results and see if it includes the class that you are interested in. The results can easily be converted to a Pandas Dataframe so that you can query them.

Here is an example to check if the results contain an instance of class 0 and then save the results if it does.

I am assuming your labels are definitely one hot encoded, which is why you are using categorical_crossentropy? If they are not, then you could give sparse_categorical_crossentropy a try.

For a measure to be used as a loss function, it must be differentiable, with non-trivial gradients.

For instance, in image classification, accuracy is the most common measure of success. However, if you try to differentiate accuracy, you'll see that the gradients are zero almost everywhere and therefore one cannot train a model with accuracy as a loss function.
Similarly, IoU, in its native form, also has meaningless gradients and cannot be used as a loss function. However, extensions to IoU that preserve gradients exist and can be effectively used as a loss function for training.

here is one way , if I didn't do any mistake :

The solution is:

As your input image size is (850, 600) (H, W) and considering that for this given image you have 4 objects, not 850 with (600, 600) masks. your masks tensor should have dimension (number of objects, 850, 600), thus your input should be: