"Quick, Draw!" was released as an experimental game to educate the
public in a playful way about how AI works. The game prompts users to
draw an image depicting a certain category, such as ”banana,” “table,”
etc. The game generated more than 1B drawings, of which a subset was
publicly released as the basis for this competition’s training set. That
subset contains 50M drawings encompassing 340 label categories.
Sounds fun, right? Here's the challenge: since the training data
comes from the game itself, drawings can be incomplete or may not match
the label. You’ll need to build a recognizer that can effectively learn
from this noisy data and perform well on a manually-labeled test set
from a different distribution.
Your task is to build a better classifier for the existing Quick,
Draw! dataset. By advancing models on this dataset, Kagglers can improve
pattern recognition solutions more broadly. This will have an immediate
impact on handwriting recognition and its robust applications in areas
including OCR (Optical Character Recognition), ASR (Automatic Speech
Recognition) & NLP (Natural Language Processing).
This code is my solution for this challenge, it is a fork of