Hello, K.Frank,
Sorry about the delay in replying. I really appreciate your thoughtful responses.


Loss function - I can understand why you’d recommend cross-entropy loss. I chose RMSE because the end users would like to be able to compare the output “error” from the model with the results from the classical machine vision approach. RMSE intuitively captures the overall deviation between an output distribution and the ideal distribution. Cross-entropy loss would be more difficult from them to interpret.


I was startled by your question about downsampling, since I thought this was a standard approach in building CNNs. Wouldn’t a network designed to process 1500x1500 images be enormous?  In any case, the particles are quite visible in a 224x224 image (see below). I’m also switching from color to gray scale. Do you think this is a bad idea?


Here’s a downsampled example (downsampled to 224x224 using ImageMagick, not Pytorch transforms):

(The images are normalized before training. This has not been.)
The image area has been cropped to a circle because I am using rotations to do data augmentation.


I think you are right about needing a greater diversity of training data. All these images were taken in the same lab, with the same lighting. I’ll have to discuss this with our end users, to see what they can do.


I recently became aware of some work doing segmentation analysis of this general sort of image using ML, specifically the Mask R-CNN model. Results are excellent, but for training, they had to draw polygons around every single particle in each training image!


My hardware resources are fairly modest, but the ML machine is more or less dedicated to that task, so I can start it up and let it run.


Thank you again for your suggestions. I will update this thread with any progress.
Best regards,
Sally