Overview on Watson Visual Recogniton
Rating: 6 out of 10
IncentivizedUse Cases and Deployment Scope
As a tech enthusiast, I'm pretty much interested in testing new products and interesting projects. This visual recognition is pretty good, AI that can easily recognize the image and classify it based on the images we trained. There are pre-trained images for a few basic image detections, you can easily customize and train the WRR(Watson Visual Recognition). It can typically be employed in inspection in real time (Like to find defected products). But I feel like it's not ready for the enterprise yet and should be improved in my perspective. I doubt its efficiency as it's not perfect yet. Though it's not perfect it's pretty good that it can predict the image with arguably better accuracy and can be used in developing simple visual recognition applications.
Pros
- 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.
Cons
- Not perfect
- Accuracy is doubted and sometimes it may not predict correctly.
- I think it should be improved and should add a few more functionalities.
Likelihood to Recommend
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.