Training Semantic-Segmentation Data Set (v0.1.0)

Posted on Fri 05 January 2018 in semantic segmentation by Kazuhiro Terao
Tagged with public data, semantic segmentation, u-resnet

A demonstration to train U-ResNet (convolutional neural network for semantic segmentation) for track/shower separation using a (practice) public data sample (v0.1.0). I show the network's learning curve as well as visualization of how the network's performance improved during the training on a specific track/shower sample image. This notebook can also serve as a generic example of configuring larcv IO to train a semantic segmentation network.

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