I’m trying to train a machine learning model to detect if an image is blurred or not.

I have 11,798 unblurred images, and I have a script to blur them and then use that to train my model.

However when I run the exact same training 5 times the results are wildly inconsistent (as you can see below). It also only gets to 98.67% accuracy max.

I’m pretty new to machine learning, so maybe I’m doing something really wrong. But coming from a software engineering background and just starting to learn machine learning, I have tons of questions. It’s a struggle to know why it’s so inconsistent between runs. It’s a struggle to know how good is good enough (ie. when should I deploy the model). It’s a struggle to know how to continue to improve the accuracy and make the model better.

Any advice or insight would be greatly appreciated.

View all the code: https://gist.github.com/fishcharlie/68e808c45537d79b4f4d33c26e2391dd

  • Zaktor@sopuli.xyzEnglish
    1·
    2 days ago

    I’m not sure what the issue is, but I can’t see your confusion matrix. There’s a video player placeholder, but it doesn’t load anything.

    Hard to tell whether the larger convolution was a bust or not. It’s got the one big spike away from the training loss, but every other epoch is moving downward with training, which looks good. Maybe try a few more runs, and if they commonly have that spike, try to give them longer to see if that loss keeps going downward to meet the training.

    For efficiency you can also increase the convolutional stride. If you’re doing a 10x10 you can move by more than one pixel each stride. Since you’re not really trying to build local structures, going 5 or 10 pixels at a time seems reasonable.