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SketchGNN
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SketchGNN
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# 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]](https://arxiv.org/abs/2003.00678) ## Requirments - Pytorch>=1.6.0 - pytorch_geometric>=1.6.1 - tensorboardX>=1.9 ## How to use **Test** ```bash python evaluate.py --dataset SPG256 --class-name airplane --out-segment 4 --timestamp BEST --which-epoch bestloss ``` **Train** ````bash python train.py --dataset SPG256 --class-name airplane --out-segment 4 --shuffle --stochastic ```` ## License MIT License
