Menu
What do each for the following losses mean? (in the TensorFlow Object detection API, while training FasterRCNN based models)
Loss/BoxClassifierLoss/classification_loss/mul_1
Loss/BoxClassifierLoss/localization_loss/mul_1
Loss/RPNLoss/localization_loss/mul_1
Loss/RPNLoss/objectness_loss/mul_1
clone_loss_1
395
The loss functions of object detection can be categorized as two sorts: the classification loss and the localization loss. The former is applied to train the classify head for determining the type of target object, and the latter is used to train another head for regressing a rectangular box to locate target object.
Loss Function The localization loss is a smooth L1 loss between the predicted bounding box correction and the true values. The coordinate correction transformation is same as what R-CNN does in bounding box regression.
Objectness Loss — Knowing One's Worth. With each box prediction is associated a prediction called 'objectness'. It comes in the place where in previous detectors like RCNN came the confidence that a region proposal contains an object, because it is multiplied by the lass score to give absolute class confidence.
TensorFlow object detection is a computer vision technique that detects, locates, and traces an object from a still image or video. The method allows us to recognize how the models work and provides a fuller understanding of the image or video by detecting objects.
The losses for the Region Proposal Network:
Loss/RPNLoss/localization_loss/mul_1: Localization Loss or the Loss of the Bounding Box regressor for the RPN
Loss/RPNLoss/objectness_loss/mul_1: Loss of the Classifier that classifies if a bounding box is an object of interest or background
The losses for the Final Classifier:
Loss/BoxClassifierLoss/classification_loss/mul_1: Loss for the classification of detected objects into various classes: Cat, Dog, Airplane etc
Loss/BoxClassifierLoss/localization_loss/mul_1: Localization Loss or the Loss of the Bounding Box regressor
162
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
