ML Models for Single-Line Drawings
Project description
singleline_models
Install
pip install singleline_models
Models
Sketch RNN
PyTorch implementation of the SketchRNN paper, A Neural Representation of Sketch Drawings.
Sketch RNN learns to reconstruct stroke-based drawings, by predicting a series of strokes. It uses a sequence-to-sequence LSTM model, with gaussian mixture heads to produce a sequence of stroke coordinates.
Datasets
data/quickdraw/
: Sample data from Quick, Draw! Datasetdata/look/
: Custom dataset of single-line drawings by @andrewlook
All data is stored in stroke-3 format, meaning a list with three columns:
delta_x
delta_y
lift_pen
(if1
, “lift the pen” and start a new stroke; otherwise0
)
Acknowledgements
- PyTorch Sketch RNN project by Alexis David Jacq
- Annotated Sketch RNN in PyTorch by LabML
- Tensorflow SketchRNN by Magenta Team and David Ha
- sketch-rnn-datasets by David Ha
- SketchRNN-Pytorch by OhataKenji
Improvements
- Log epoch and learning rate
- LR decay
- ETA decay (for KL loss)
- Dropout
- Layer Normalization
- Recurrent Dropout
Project details
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