Share your work here ✅

thanks for the add to the huggingface study group! currently building a model to identify different running shoe types, will add it here as soon as i’m done :slight_smile:

1 Like

update: finished building my model (after spending about 5-6h on it)!
In the world of running, there are quite a number of running shoe types - as a volunteer coach, I constantly get questions as to which exact model of shoes to buy - because wearing the wrong types of shoes for different workouts could lead to injuries, or it might burn through the effective mileage of the shoe quicker (therefore needing a new replacement in a shorter time period). In reality, shoes can be classified into several major categories:

  1. “everyday” running shoes - regular workhorses that you can do most of your easy runs in
  2. tempo running shoes - shoes for quicker speeds, track workouts, but not to be used for racing
  3. racing running shoes - shoes that are used purely for racing and nothing else (to prolong livespan)
  4. stability / maximal cushion running shoes - shoes that are designed to ‘prevent injury’ [although such claims of whether injury is truly prevented are disputed]
    So I built a running-shoe type classifier! You can find it it here, with the source code here

A very humble customizable “Is a bird or not” exercise where the user can easily customize the image search keywords and number of epochs. Interesting to analyze how error rate changes using different search keywords.

https://www.kaggle.com/code/julio4ai/customizable-is-a-bird-or-not

Not sure about how the last part works, but apparently it does. I thought that the last line should be:

print(f"Probability it is a {keyword1}: {probs[0]:.4f}")

But that does not work as I expected, so I changed to:

print(f"Probability it is NOT a {keyword1}: {probs[0]:.4f}")

Maybe one of the most veteran mates can share some thoughts about it. Thanks in advance!

If you print probs you will see that it’s a size 2 tensor:
image

it means that the first value is the probability of the item being in the class 0 and the second value is the probability of it being in the class 1, that’s why if you pick probs[0] you will always have the probability of the item being class 0, but you should remove the “NOT” and use

print(f"Probability it is a {keyword1}: {probs[0]:.4f}")

I just ran your notebook and it works well

That was fast and clear. Many thanks, Kamui!

1 Like

sorry julio I got confused you should drop the “NOT” as probs[0] is the probability that the target is keyword1
so you should remove the “NOT”

1 Like

I got the point. Thanks again!

1 Like

Hello,

my name is frank, i’m from germany, and I realy like this awsome course.

To get an understanding of what happens during backpropagation i have created a small notebook - mainly to understand this delta rule better. I would be interested in feedback from the community - is this correct as I have outlined it or am I wrong here and there.

I made it in colab here is the link - you should open it in colab - in others it looks bad (I use a lot of tables).

then thank you already all who look at this

frank