Project hosted at Stanford University examining developmental changes in children's drawings
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README.md

README.md

kiddraw project

Drawings as a window into developmental changes in object representations

Inspired by the availability of this large and public dataset containing drawings of various visual concepts (Google quickdraw-75: https://github.com/googlecreativelab/quickdraw-dataset), this project asks how the ability to express these visual concepts in drawings develops.

*To reproduce our CogSci 2018 submission:

  1. Experiment code in Experiments/museumdraw. Other directories are in development. --Needs to be spun via node.js on a server

  2. Recognition rating code in experiments/ratings/recognition_ratings --Recognizability ratings run on amazon mTurk --Outputs preprocessed data for using in Rcode in writing/

  3. Analysis/museumdraw/python: Many scripts require access to GPUs to extract features from VGG-19 efficiently.

--render_quickdraw.ipynb: ##Code to render .pngs from QuickDraw database

--extract_all: ##Bash script to extract all vgg-19 features.

--preprocess_musemdraw_e1.ipynb.ipynb ##Pulls drawings from server, renders pngs, computes low-level covariates (drawing time, number of strokes, mean intensity), saves out

--analyze_features_museumdraw_cogsci_archive.ipynb ## Jupyter notebook that analyzes vgg-19 features from pool 1-5 and fc6/fc7. Creates layerwise and RDM figures for use in R code in kiddraw/writing.

  1. Writing:

--Contains R scripts for rendering entire CogSci paper; analyzes recognizability ratings from scratch. Imports outputs from analyze_features_museumdraw_e1.ipunb