Hyper Parameter

Hyper-Parameter Explanation

QA is shipped mostly fully configured and the few hyper-parameters of interest are easily modifiable via the configuration file. These hyper-parameters can be set in the config.ini file

• [common]
• [flask]
• [cuda]
• [sqlalchemy]
• [pooling]
• [train_ae]
• [train_tl]
• [make_patches]
• [make_embed]
• [get_prediction]
• [frontend]
• [superpixel]

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When annotating different primitives, the user needs to focus on hyper-parameters, edgeweight, approxcellsize, and compactness.

• edgeweight: The edgeweight is hyper-parameter in [train_tl]. Setting a higher edgeweight encourages the DL model to focus the loss function on incorrectly classified boundary pixels; increasing this weight is beneficial when clear boundaries are hard to distinguish.
• approxcellsize: The approxcellsize is hyper-parameter in [superpixel]. This is set to the approximate width of the desired superpixel, and works well when set to the approximate width of the object of interest.
• compactness: The compactness is hyper-parameter in [superpixel]. The nonnegative compactness value determines the regularity of the superpixel boundary, wherein higher compactness encourages superpixels to retain their initial square shape, while lower compactness allows for greater boundary irregularity.