Loss stuck for regression model

I'm training a model that returns 2 parameters. These two parameters are used for classical image processing:

1. a threshold for the kirsch-operator
2. the number of iterations for billateral filter.

The model trains using 300 representative images, along with both parameters that were manually determined.

• I am currently using resnet18. A convolutional regression model.
• The fully connected layer is changed to output 2 nodes.
• As loss function I've chosen is the mean squared loss.
• Reducelronplateau is used as a learning rate scheduler to minimize validation loss.

Unfortunately, my validation-loss is stuck. It settles inbetween 2000 and 3000.

Here are some of the things I tried:

1. Experiment with different models, including resnet36 resnet50 vgg16 mobilenet.
2. Resized and changed the batchsize.
3. Multiple heads after the renset18 feature layer for both output and calculated loss, seperately for iteration and thresholding
4. Ssingle output model for each threshold and iterations separately.
5. Replaced RGB-images tried HSV-images.

I'd really appreaciate suggestions on how to succeed. Thank you very much.