In terms of the actual loss values, it’s difficult to say what is “normal” as they can vary widely depending on the factors mentioned above. However, some practitioners have reported achieving classification losses around 1.0-2.0 and regression losses around 0.1-0.2 for SSD MobileNet v1 on common object detection datasets like
SSD mAP, or mean average precision, is a metric used to measure the accuracy of object detection algorithms. It is calculated by taking into account the true positive rate, false positive rate, and precision of an algorithm. The higher the mAP value, the better the algorithm’s performance.
Common mAP
Learning rate in training an SSD model is the step size used when updating the weights of the model during backpropagation. It determines