Use Custom Image Recognition Collection locally

Question

I have created a Custom Image Recognition collection on IBM Cloud and am using it in my Django website to do the processing. However, I noticed that the response time ranges from 6 to 14 seconds.

I want to reduce this turnaround time. I am already zipping the image file that I sent. So when going through the API reference document here on IBM Cloud I noticed that there is a method called "get_model_file" which download the collection file to a local space.

But no documentation on how this can be used. Anyone who has successfully implemented this? Or am i missing something here?

Answer 1

However, I noticed that the response time ranges from 6 to 14 seconds. I want to reduce this turnaround time. I am already zipping the image file that I sent.

How many images at at time are you sending in the zip file to the /analyze endpoint? If you are just sending one image at a time, you should not bother zipping it. Also, if you can, you should parallelize your code so that you make 1 request per image, rather than sending, say 6 images in a single zip file. This will reduce the latency.

Using the v4 API, by the way, you should resize your images to no more than 300 pixels in either width or height. In fact, you can "squash" the aspect ratio to square and it will not affect the outcome. The service will do this resizing internally anyhow, but if you do it on the client side, you save network transmission and decoding time.

With a single image at a time, if your resolution is under 300x300 pixels, you should have latency under 1.5 seconds on a typical call, including your network transmission time.

Answer 2

As the documentation states

Currently, the model format is specific to Android apps.

So unless you are creating an Android App then this is not going to work for you.

You probably have two areas of latency. First will be from the browser to your Django app. Second will be from your Django app to the Visual Recognition service. I am not sure where you have hosted the Django app, but if you locate it in the same region (data centre would be even better) you might be able to reduce part of the latency.