The task of recognizing game units in the screenshot I'm new to computer vision and I want to solve the task of recognizing the game units of the game Clash Royale in the screenshot.
Briefly, there are about 70 different types of gaming units belonging to two teams (they differ a little in colors and some are visible in front, others with backs). I want to find game units on the screenshot and classify their type of unit (then health and the team).



What are the best tools for task like this? What libraries will I use to simply teach the model? How many teaching examples do I need to learn the model? It seems that the quality of the screenshots is quite good and the images are clear. To what size should I reduce the screenshoot to get a good model and its speed? Maybe someone had a similar experience?

I think about CNN or a lot of HAAR cascades for each of the types of units, but I would like to receive advice.
Machine learning doesn't seem needed, since the image of each unit is always identical.  One approach is to obtain a clean image of each unit, and then use template matching to find all locations where the template occurs in the screenshot.  You might be able to check all locations in the screenshot whether they exactly match the template image (you will probably want to mask out the part where the health appears, as that will obscure the image).  This can be done very efficiently.

To recover the health, you might also be able to do template matching on the numbers 0-10.

The way to tell what resolution you can downsize to is empirically: you experiment with different resolutions and measure the accuracy for each.  There's probably no way to predict without trying it.

If you want to try machine learning, you could try a CNN, or better yet, you could try retraining the YOLOv3 object detector for your particular images (see, e.g., the YOLO project page or here).  You will probably need a larger training set this way, and you'll have to do a bunch of manual annotation of sample screenshots.

Tool and library recommendations are off-topic here.