Object Detection in Detectron2
The details
Here is a list of the parameters that you can try to play around with in Detectron2.
Process
Region Proposal Network (RPN): The RPN is responsible for generating object proposals in an image. It takes an image as input and outputs a set of bounding box proposals, along with their objectness scores. These proposals serve as potential regions of interest (ROIs) for further processing.
MODEL.RPN.PRE_NMS_TOPK_TRAIN: The RPN proposals are ranked based on their objectness scores. This configuration option determines the number of top-scoring RPN proposals to keep before applying non-maximum suppression (NMS) during training. It limits the number of proposals to reduce computational overhead.MODEL.RPN.NMS_THRESH: This option sets the IoU (Intersection over Union) threshold for NMS during training. Proposals with IoU higher than this threshold are considered redundant and suppressed, keeping only the highest-scoring proposal.
ROI Heads: The ROI Heads module takes the region proposals generated by the RPN and performs object classification and bounding box regression on these proposals to obtain the final predictions.
MODEL.ROI_HEADS.IOU_THRESHOLDS: This configuration option specifies a list of IoU thresholds to assign ground-truth labels to the region proposals during training. For example, if the IoU threshold is set to 0.5, any proposal with an IoU greater than or equal to 0.5 with a ground-truth box will be labeled as a positive sample.MODEL.ROI_HEADS.NMS_THRESH_TEST: After classification and bounding box regression, NMS is applied to the predicted boxes to remove redundant detections during testing. This option sets the IoU threshold for NMS during inference.MODEL.ROI_HEADS.SCORE_THRESH_TEST: This option sets the minimum confidence score threshold for retaining detections during inference. Detections with scores lower than this threshold are discarded as false positives.
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