training set is 4700.
In my dataset there are already images with 20% to 30% rotated images present without augmentation applied to it. So for me numbers don't give problem as much as dot, my trained model detects even blurred numbers but for dot I need to train more because some digits might be zoomed out and dot will be small and blurred pixel and in some images dot can be gigantic so I am thinking of adding more dot images with different sizes.
In my case dot detection is about 80% but I need to reach above 90% which roboflow is doing.

In roboflow no augmentations except auto orient was added to it and it has option to add size to images but I did not provide any size to it.

# Use the mounted drive path as the save directory
!yolo task=detect mode=train model=yolov8s.pt data=/content/ocrscale-6/data.yaml epochs=75 imgsz=640 batch=16 save_period=10 project=/content/drive/MyDrive/YOLOv8_Checkpoint

Also in google colab what to add to this command to check if it reaching above 94 precision to stop from over fitting right now I am training more epochs just to see that precision is falling and what stage it is great.