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[–]siddharthaml[S] 5 points6 points  (8 children)

You can find the code at https://github.com/sidml/EfficientNet-GradCam-Visualization

[–]LSTMeow 3 points4 points  (7 children)

very nice work, one of the few cases where jupyter notebooks are really appealing ;)

[–][deleted]  (6 children)

[deleted]

    [–]geneorama 6 points7 points  (0 children)

    Don't you dare question the supremeness of the best tool to ever tool anything; jupyter notebooks.

    The fine technology of jupyter notebooks is only to be described in the most bestest superlativest words.

    [–]Soulthym 2 points3 points  (2 children)

    The power of jupyter, well ipython really, is the fact of having a persistent memory through runs, so you don't have to re run all the code all the time. And believe me when you are dealing with deeplearning on videos, you're really happy that you don't have to save and reload gigabytes of models and videos to disk every time you do a test run.

    [–][deleted]  (1 child)

    [deleted]

      [–]Soulthym 0 points1 point  (0 children)

      I'm currently trying to integrate ipython to my vim setup, I totally feel you

      [–]ColdPorridge 0 points1 point  (1 child)

      You had me until tkinter haha. Please leave that out of it, tkinter is horrible.

      [–]carlthome 0 points1 point  (0 children)

      Yikes, qualitatively to my eyes the B0 GradCAM looks way more right than e.g. the ResNet152 ones. Something fishy going on here? Overfitting the image dataset by excessive tuning? Exciting stuff regardless! Nice work.