IBM Watson Visual Recognition (discontinued) vs. Keras

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

Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch

Incentivized

• Food recognition
• High reliability
• Speed

Incentivized

• One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
• It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
• It also provides functionality to develop models on mobile device.

Incentivized

• 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.

Incentivized

• As it is a kind of wrapper library it won't allow you to modify everything of its backend
• Unlike other deep learning libraries, it lacks a pre-defined trained model to use
• Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs

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

I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.

Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.

Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer Keras as it is easy and powerful as well.

Incentivized

• High cost if models are trained

Incentivized

• Easy and faster way to develop neural network.
• It would be much better if it is available in Java.
• It doesn't allow you to modify the internal things.

Incentivized