anirbankonar123 / corrosiondetector Goto Github PK

Hello!

Thanks a lot for releasing your code and the blog post at Floyhub. I have been learning new tricks with Tensorflow.

I have one question for you though, in short, in the code below for rust image classification, how do you know that if output > 0.5 the image corresponds to class RUST, and if output < 0.5 it corresponds to NORUST class?

To me it seems it could also be the other way around (if output < 0.5 class is RUST, else class is NORUST) because we (the user) never defined this. I have looked throughout the documentation but I have not find an answer for this.

if (rust_prob > 0.5):
    print("This is a Rust image")
else:
    print("This is a no Rust image")

When doing binary classification and the ImageDataGenerator, the training details for the loss function are hidden from the user. In other words, how do we know for which class from the ImageDataGenerator the network was trained to output a probability > 0.5.

A more general question, but not related to your tutorial, for multiclass classification, if for example the last layer has N neurons corresponding to N classes, how do we know to which neuron the network assigns which class?

Again, thank you very much for your tutorial, it has been very useful and I have learned several new things!

Juan

Hi,
I am trying to follow this application and I am not able to get matplotlib.pyplot into my Anaconda python 3.6 setup aunder tensorflow_env I have created for use in Jupyter notebook. I have matplotlib installed. I cannot locate where pyplot is available.

It fails on line 10 upon loading ... import matplotlib.pyplot as plt

How can I resolve this issue?

Thanks

Hello there,

which license is managed?

Today we have a hackathon in our company.
to detect the corrosion in steel elements using image processing
I'm using your opensource contribution it is very helpful.
I'm getting this error like this
----WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches (in this case, 4 batches). You may need to use the repeat() function when building your dataset.

pls help me.
It will be very helpful