Part 1 Lesson 1 -- Men vs. Women

I am trying my hand at Lesson 1 substituting Men vs. Women in place of Dogs vs. Cats. I have run everything from lesson1.ipynb on a new file on my paperspace machine and seem to have a solid 80% accuracy rate using only 100 total images (60 Men && 60 Women Train, 20 Men && 20 Women Valid, 20 Men && 20 Women Test).

My question is what did I do wrong with the learning rate. In [49] : learn.sched.plot_lr() on my notebook shows a straight line. There is no slope like shown In [36] lesson1.ipynb. Further, my In [50]: learn.sched.plot() shows nothing. There is no plotted line (loss vs LR). This has me now questioning my supposed 80% accuracy. It doesnt make sense to me after watching Lesson 1 to have any accurate model if I cannot discern an accurate-ish learning rate.

I posted an image of my workbook on imagebin:

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I think this is due to the batch size (bs) as training on small size datasets. Not explicitely setting a bs for your data gets you to train your model on a default batch size of 64.
Try reducing the batch size like so:
* data = ImageClassifierData.from_paths(path, tfms=transforms_from_model(arch, sz), bs=10)
and see what comes out.

Hope it gets better.

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Thank you so much for your help! That makes sense to shrink the batch size! It has also given me a curve on the learning rate scheduler. However, the validation loss x learning rate (log) is still empty?

Note quite sure, but Can you try running your code in this order:

  • lrf = learn.lr_find()

  • learn.sched.plot()

  • learn.sched.plot_lr()

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Thank you for your time @StCarl. Unfortunately, I got the same thing. Something else I recall from Jeremy’s lesson was his lrf stopped at 75% as it had finished improving. Mine seems to go all the way to 100% Hmmm…

Hi @theyear2000, this link might help:
Lesson 1: Choosing a learning rate doesn't work on my dataset

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@StCarl thank you so much for the help. That did it! I started playing around with the batch size lowering it until I found a sweet spot that raised my accuracy to 88%.

data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz), bs=4)

I really appreciate your time.


Good to hear that. How does learn.sched.plot () look like?

This was addressed as a question In Part 1, Lesson 3 at time 1hr 59min in case it helps anyone in the future…

Something else you can try and see if it’ll increase the accuracy is to extract the faces from the images and run your model on the faces themselves.

Interesting idea! Do you mean add the extracted faces to the existing data set or replace the existing data with the extracted faces? Thanks for the heads up!