Model of the car
What you want to do is close to one-shot image recognition. You have not 1, but 3-4 examples of each car, but that is still a small amount, especially considering the car looks different from different angles (are you supposed to recognize them from any point of view, including sideways, rear, front, and 45 degrees etc.? maybe you also want to recognize them photographed from the top?).
One interesting article I found is: Siamese Neural Networks for One-shot Image Recognition by Koch, Zemel, and Salakhutdinov.
I also found that Caffee supports Siamese networks.
You may want to read other literature about the One-shot learning.
One trick you can do is to utilize the fact that cars are symmetric, so you can double the number of learning examples by reflecting each image.
Determining the color is not as simple as it seems. Your algorithm need to determine where is the car at your picture, and then determine the color, taking into account the lighting conditions, as well as light effects, most notably reflection. For example, consider the following image: .
We see strawberries as red, but there are no red pixels on this picture. The images of strawberries consist on grey pixels.
Maybe you also need a convolutional neural network, or just a neural network, for this task.