Closed VGrondin closed 1 year ago
Hello! DropLoss is a technique that lowers the loss for proposals with an IoU (Intersection over Union) with ground truths that are smaller than the self.droploss_iou_thresh value. If you set a high threshold, the model will explore more regions.
To understand better, let's take an example of three proposals (P1, P2, P3) with IoUs of 0.002, 0.02, and 0.2, respectively:
Using a higher threshold will lead to a higher drop rate, but there is a trade-off between exploration and exploitation. Simply encouraging the model to explore new regions may cause it to fail to fit the training data, leading to meaningless predictions.
I see, so increasing by only 1% the "tolerance" for bounding box IoU has a surprisingly big impact on model training. Very interesting and thanks for the clarification.
Hello, I am a bit confused with the droploss threshold. From my understanding, a high threshold = more loss (the model is penalized for exploring), while a low threshold encourage the model to explore. This seems to be the case when I look at the code:
However the wording in the paper says the opposite: