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
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
clone_loss_1 is relevant only if you train on multiple GPUs: Tensorflow would create a clone of the model to train on each GPU and report the loss on each clone. If you are training the model on a single GPU/CPU, then you will just see clone_loss_1, which is the same as TotalLoss.
The other losses are as described in Rohit's answer.
There are four losses that you will encounter if you are using the faster rcnn network
1.RPN LOSS/LOCALIZATION LOSS If we see the architecture of faster rcnn we will be having the cnn for getting the regoin proposals. For getting the region proposals from the feature map we have the loss functions . This is the localization loss for bounding boxes for the anchors generated.'
2.RPN LOSS/OBJECTNESS LOSS This is also when we are extracting the region proposals whether the object is present in the anchorbox or not.
3.BOX_CLASSIFIERLOSS/CLASSIFICATION_LOSS This is at the final layer to which class the object belongs to whether dog or cat??
4.BOX_CLASSIFIERLOSS/LOCALIZATION_LOSS This is also at the final layer for the bounding boxes of the object. (coordinates for dog and cat)
