An NN is like a stack of pancakes, with computation flowing up when we make predictions.
How does it all work?
THREAD: How is it possible to train a well-performing, advanced Computer Vision model 𝗼𝗻 𝘁𝗵𝗲 𝗖𝗣𝗨? 🤔
At the heart of this lies the most important technique in modern deep learning - transfer learning.
Let's analyze how it
THREAD: Can you start learning cutting-edge deep learning without specialized hardware? \U0001f916
— Radek Osmulski (@radekosmulski) February 11, 2021
In this thread, we will train an advanced Computer Vision model on a challenging dataset. \U0001f415\U0001f408 Training completes in 25 minutes on my 3yrs old Ryzen 5 CPU.
Let me show you how...
An NN is like a stack of pancakes, with computation flowing up when we make predictions.
How does it all work?
An image is a collection of pixels. Each pixel is just a bunch of numbers describing its color.
Here is what it might look like for a black and white image
Each layer performs computation on the image, transforming it and passing it upwards.
The outputs of a layer are called activations, and the outputs of the last layer have a special meaning... they are the predictions!
We would like for it to have a high value, and for other activations in the last layer to be small...
So far so good! But what about transfer learning?
We know that these layers evolve during training to become feature detectors.
What do we mean by that?
A layer above might have windows that construct shapes from these lines.
We might have a window light up when it sees a square, another when it sees a colorful blob.
Maybe one sliding window will combine lines and detect text... maybe another one will learn to detect faces.
Does all of this sound like a hard task?
But, presumably, once we detect all these lower-level features, we can combine them in a plethora of interesting ways? 🤔
Or trains, planes, and ships. Or blood cell boundaries. Or aneurysms in x-rays. The possibilities are endless!
We let researchers, large corporations, spend millions of dollars to train very complex models.
And then we get to build on top of their work! 😇
But so much for the theory. How does it all work in practice?
The @fastdotai framework downloaded the model for us and removed the top of it (the part responsible for predicting 1 of 1000 classes).
During training, we kept nearly the entire model frozen, and only trained the uppermost part, making use of all the lower level features that were being detected.
Ingenius! 😁
We will hopefully get a chance to explore all of them 🙂
If you enjoyed this thread, let me know please and help me reach others who might also be interested 😊🙏
And the visualizations of what the layers can detect?
Next stop - deciphering how it all works in code and finding ways to further improve our model!
Stay tuned for more 🙂
