IBM Watson Visual Recognition (discontinued) vs. Quantum Boost

As I mentioned before, it can only be employed in simple basic visual recognition applications. It can be employed in large projects and relying it on completely is not encouraged. It's better to create your own algorithms rather than using it. If you are from a non-programming background, then I may suggest you rely on this and use it to develop simple apps that can predict a few plants and animals.

Incentivized

• Easy to use interface, you can just drag and drop the images in the negative and positive dropboxes to train.
• It's affordable and there is a free version to test for yourself and check if it's useful for you.
• Easy to integrate with apps using one single API key and you can train easily with your terminal.

Incentivized

• Documentation sometimes not complete

Incentivized

I am giving this rating on the basis of its usability in real-time applications and based on the interface to upload negative and positive images to train the AI. But it's not perfect and sometimes its predictions are wrong. On overall usability, it's better if you are planning on working with UI rather than using complex programs and algorithms on your own.

Incentivized

• High cost if models are trained

Incentivized