Bad Predictions from MRCNN

Question

I am getting very poor training and validation results. I am trying to train on a custom dataset using coco_weights as a starting weight. There is only one instance of the object in every image. Is it possible my config parameters setting is not good?

IMAGES_PER_GPU = 1
TRAIN_ROIS_PER_IMAGE=300

NUM_CLASSES = 1 + 1 

STEPS_PER_EPOCH =100

DETECTION_MIN_CONFIDENCE = 0.95
VALIDATION_STEPS = 50
MAX_GT_INSTANCES = 1
DETECTION_MAX_INSTANCES = 1

USE_MINI_MASK = True

I set DETECTION_MAX_INSTANCES to 1 because there is only 1 object in all images. i have tried other configurations though with no result I also set MAX_GT_INSTANCES to 1 because all training images also have only one image, even though I have also tried large values with no result. I have also tried varying values for TRAIN_ROIS_PER_IMAGE I have set USE_MINI_MASK to both true and false. Also, I need to know what values would work best for IMAGE_MIN_DIM,RPN_ANCHOR_SCALES

Also, I suspected the TensorFlow version. Was obtaining poor results using Tensorflow 2.6.0. I then tested writing a basic program predicting a simple coco image using coco weights and was obtaining a result predicting 4+ bbs when there was only 1 object. This changes after downgrading to 2.5.0 and I was able to get an accurate prediction on the basic image. However, using Tensorflow 2.5.0, I am still getting poor training and validation results on my custom dataset. Please any advice would be appreciated

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