: Image Classification with 50 animal drawings from Quick Draw game data at Google
"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. More details can be found here.
This project is for building an image classifier model that can handle noisy and sometimes incomplete drawings and perform well on classifying 50 different animals. Starting from a simple CNN as a baseline, I used Residual Net and VGG19. I choose these models because they are covered in Andrew Ng's DL specialization course On Coursera. I wanted to have a time to practice what I learnt in class.
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Project Date: Jan, 2019
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Applied skills: Image Processing and visualization. Parallel Computation with Dask. Image Classification with CNN and sequence model.
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Doodle.ipynb : Image processing and visualizing the drawings. Building a simple CNN as a baseline on Kaggle
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1. Preprocessing.py : 1. data preprocessing
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2. Baseline.py : 2. Baseline Modeling
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3-1. ResNet50.py : 3-1. Bench Mark modeling (ResNet50)
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3-2. VGG19.py : 3-2. Bench Mark modeling (VGG19)
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doodle.zip : The dataset extracted only the animal csv files