I've recently started learning CNN's. I need a CNN that is specialized for insects detection. Dead insects will be put on a piece of paper / container, then images will be taken from a same distance, and are at same pixels.
I am expecting to prepare my own training datasets: - Thousands of images of a particular insect, let's say a housefly - Each image contains only one housefly - Consistent lighting for the photos taken
Then, I will start to train the CNN, save the model and so on. I'm not sure about the following:
During operation of the model, will it still be able to detect the housefly if there are other insects in the image? (not covering the housefly of course)
If I want my model to recognize housefly only, so the training datasets contain housefly only, true or false?
If later I want my model to recognize 2 types of insects, housefly and ant, what type of datasets I need to prepare?
I will eventually need to count the amount of houseflies in an image. Can anyone direct me to a correct path towards feasible methods?
Does anyone has useful resources that explain on how to determine the suitable amount of convolutional layers (CL) and fully-connected layers (FCL)? The default seems to be 2 CL and 2 FCL, but I am wondering why not more than that?