Computer Vision is the one domain where ML (and neural networks in particular) is the undisputed king. Unless you're in an embedded application where you can't

Computer Vision is the one domain where ML (and neural networks in particular) is the undisputed king. Unless you're in an embedded application where you can't use neural networks in which case you might want to go with handcrafted features to trade-off accuracy for speed/compute-efficiency. With regards to the size of your dataset, there's no hard rules it depends on the complexity of the task. Problems with a high number of classes are among the more difficult ones, 100 samples per class might be enough or it might not . The only way to know for sure it to try and see if you reach a performance that's acceptable for your application. I recommend the Pytorch framework, it's coherent, easy to use and well documented (both the API reference and the examples available on SO and throughout the net). Your problem is similar to imagenet (assuming you want to detect the presence of a class and not its position, in which case it's a different problem.) so you can try to run one of the pytorch tutorials and see how well it does. The only difference is you want to detect multiple classes in one single image so you'll have to adjust your output layer and loss potentially but the network itself could remain the same. You also might want to look into doing transfer learning with a pretrained imagenet network to speed-up the training.