I assume you’ve labelled/segmented only a portion of your images using labelbox, and you are looking for machine learning to label the remainder? You could treat it as muliclass (classes == male preadult, male adult, female preadult, female adult) or multilabel (classes == male, female, preadult, adult). Or you could tackle it as two separate tasks, first identify gender then maturity. I’d go with the first option and use the fastai camvid notebook as a guide to how to use a Unet to segment images.
The other bullet points are “easily” treated without machine learning. That is, count the number of lice in an image, calculate the mean grey value of each lice, and calculate the louse size/area. You could use machine learning, but using cv2 is far easier in my view. Once you have a segmented mask from your camvid like model, you can use cv2 functions like findcontours and contourarea to get object stats, and then numpy to establish colour/grey stats. There is definitely a learning curve to cv2 and/or similar packages like scipy, but you probably need to learn it to get the most out of such segmentation tasks.
You could tackle it as an object detection problem (see [Kaggle] Discussion on Global Wheat Detection Challenge) and while it’d give you the number of lice, it wouldn’t cover what you need in terms of the light/darkmeasurement.
Now there are neural network approaches to counting/estimating objects. I might turn to them if I was counting people in a crowd, or oranges in a pile, or maybe rice grains on a plate. But if your lice are generally not touching one another much, or are more than say 60 pixels in size, and you don’t have hundreds of lice on a dish I’d stick to morphology (cv2). You can look at various kaggle competitions like https://www.kaggle.com/c/data-science-bowl-2018/notebooks to see what’s possible.
If this doesn’t help, see if you can post an image so people understand what about the task you are finding difficult. Each use case means diferent tools are most appropriate so it’s hard to give a general answer.