SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

We introduce SketchGNN, a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph, with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation and has magnitudes fewer parameters than both image-based and sequence-based methods.

[Paper]

Requirments

• Pytorch>=1.6.0
• pytorch_geometric>=1.6.1
• tensorboardX>=1.9

How to use

Test

python evaluate.py --dataset SPG256 --class-name airplane --out-segment 4 --timestamp BEST --which-epoch bestloss

Train

python train.py --dataset SPG256 --class-name airplane --out-segment 4 --shuffle --stochastic

License

MIT License