Now that the Quickdraw! competition is over, I added my solution to the repository github.com/radekosmulski/…. It is a model consisting of outputs of res50, incv4 and an rnn along with country code embeddings fed to an fc classifier. The most interesting aspect of the architecture
is how little code wiring everything together required thx to the @fastdotai lib. I replaced the classifier during training (while retaining the other weights) which the @fastdotai / @pytorch combo made very easy to accomplish.
In some sense there is nothing particularly
In some sense there is nothing particularly
interesting about my solution above and beyond the basic motions of building a model. For various reasons I didn't get to the point of applying competition specific insights. The model is also partially trained
The winning solution was a CNN ensemble done using LightGBM. It also
The winning solution was a CNN ensemble done using LightGBM. It also
used a clever way of tuning predicted label probabilities based on correctly inferring the distribution of the test set. You can read the write up here: kaggle.com/c/quickdraw-do…. Congrats to the winners!
What are my personal takeaways from the competition? @kaggle is unbelievably
What are my personal takeaways from the competition? @kaggle is unbelievably
good for learning, esp if you join a competition early. At the same time, each competition (despite what it might seem initially) turns out to be a non trivial research project which takes a lot of time. In the words of @dhh from his new book, by saying yes to a single thing you
say no to a thousand other things. The opportunity costs can be high and need to be evaluated in the context of what one hopes to do down the road.
For over a year now I have compulsively jumped from a DL project to a DL project striving to maximize learning so maybe this is a
For over a year now I have compulsively jumped from a DL project to a DL project striving to maximize learning so maybe this is a
good time to start taking things more slowly and consider alternatives.
At the same time, @kaggle just launched a super exciting whale identification competition kaggle.com/c/humpback-wha… that I would love to get into🤷♂️Here is a great overview of a solution to an earlier version
At the same time, @kaggle just launched a super exciting whale identification competition kaggle.com/c/humpback-wha… that I would love to get into🤷♂️Here is a great overview of a solution to an earlier version
of this competition: kaggle.com/martinpiotte/w…. A really great read and so many fun things one could do / try!
