I ran several YOLOv4 model training. The training AP per class and overall MAP looks good.
However, during inference on images for the ‘Person’ class, the confidence scores vary from 0.1 to 0.99. With YOLOv3 and the same dataset, we consistently achieve better detections above 0.90 and lower false positives.
Due to the issue of varying confidence scores, we are not able to configure the confidence threshold according to good detections and minimize false positives.
Our loss values, validation loss, and AP score per class are not problematic. However, despite the convergence of loss and validation loss, we are encountering low confidence scores for detected classes in our inference results.
Could you download KITTI dataset mentioned in the notebook and train against it to check if there is the same behavior? Thanks.
More, what is the mAP when you train with YOLOv4? And for your own dataset, did you use kmeans to generate anchor_shapes based on your dataset? Also, please try to enlarge “loss_class_weights” a bit and retrain.