Lesson 1 part 1 v2 custom images

Thanks so much!

@radi http://forums.fast.ai/t/how-to-scrape-the-web-for-images/7446/15 may help for downloading images.

Ok here is my first attempt :slight_smile: Hammers vs Screw Drivers …
GitHub: Lesson 1 HW Hammer vs Screw Driver

  • train folder = 47 pics of each category
  • valid folder = 11 pics of each category
  • played with the Learning Rate
  1. Learning Rate is just right … good accuracy of 91%:

  2. Learning Rate is too small …accuracy stagnates at 54%:

  3. Learning Rate is too big … accuracy is bad at 41% and the loss just explodes!!!

  4. No idea why “learn.sched.plot()” is linear:
    leanr%20plot_why%20linear

  5. Finally …
    confusion%20matrix

  6. Some interesting results:

Here are some of the ancillary things I used/learnt:

  • Images by web scrapping: https://github.com/hardikvasa/google-images-download
    ** ran from Windows cmd prompt, no need to call Python prompt, ran without the dollar sign, did pip install
    ** unless specified, it downloads the pics to the ‘Downloads’ folder

  • Image folder structure for Paperscape cloud machine: How to use your own Dataset for Lesson 1

  • Considered using PuTTY to transfer files to Paperscape cloud machine: https://it.cornell.edu/managed-servers/transfer-files-using-putty
    ** but used Jupyter Notebook in Chrome browser to upload the zipped folder but could not unzip inside the browser
    ** went back to Paperscape console to unzip

  • Change PATH in the Jupyter notebook code from the cats vs dogs example to what ever you chose
    ** there were couple of other locations where the phrase cats/dogs had to be renamed to your classes

  • Password for Paperscape cloud machine could not be copy pasted, I have to type it every time :face_with_raised_eyebrow:

  • If you cannot access the Jupyter Notebook in the browser then check if your network provider is blocking port 8888

  • If you frequently stop and start Jupyter Notebook then the default port 8888 can become unavailable and Paperspace machine will start opening Jupyter Notebook with subsequent ports like 8889 etc

Thanks :+1:

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Update to the Most Uncertain screw driver:
@jeremy may have answered why this last screw driver has such an ambiguous score of 0.48. According to his Lesson 2 DL 2018 (https://www.youtube.com/watch?v=JNxcznsrRb8), the software is written in such a way that the test set pictures are cropped to square shape, so the middle of this picture is not that obvious. It can be overcome by data augmentation, which I did not play with in my 1st homework. Thanks @jeremy for such an awesome tutorial :hugs:

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Github link to this please.

Hi, I tried to classify office building v/s outdoor field site. I put 12 images in training and 4 images in validation. I was expecting to get good accuracy but classifier’s accuracy ranges from 50 to 100 in the number of time I have run. Any idea why the classifier fails here


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