The training results of YOLOv5 are marked incorrectly

I am a beginner in YOLOv5, and I am training a custom model.
I used Roboflow to create a dataset, which includes a Training Set with 2,000 images, a Validation Set with 183 images, and a Testing Set with 115 images.
The results I get from training are not satisfactory, as seen in the image below, the position of the cueball is always significantly off. I don't know why this is happening and I am wondering if there are any ways to improve this.

result1 \

result2

This is the command I use during training.
python train.py --img 416 --batch 32 --epochs 500 --data data.yaml --weights yolov5l.pt --cache

I have tried using other YOLOv5 models, but the results are still the same. I have also continuously added data to my dataset, but there has been no improvement in the results. I would like to ask if anyone knows whether I should continue training or if there are other methods to improve the results?

The following are some indicator data about the dataset. Although I have 5 classes, I only used two of them, cue and cueball, during training.

The position of the cue ball is actually pretty spot on. If you notice the bounding box for the actual cue has a high confidence (0.76) for result 1 and 0.82 for result 2. And the rest are detections with low confidence. Judging from those two images, i guess that your recall is very high, but your precision is a bit low. You can apply a higher threshold (basically saying to filter out the ouputs under a certain confidence). You can set a default threshold of 0.5, and see if it works for your use case or not, then decrease it or increase it depending on what you want.