Hyper Parameter - choosehappy/QuickAnnotator GitHub Wiki
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]
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.