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Lobe – Perform Machine Learning Without CODE

This article was published as a part of the Data Science Blogathon.

Overview

Machine learning is an area that is very fascinating for everyone. There has been continuous effort different companies are putting in to make it available for the mass to use.

There’s been a continuous effort in the direction to make the machine learning and AI capabilities available for the Life science, medical and agriculture sectors. And if you really want to make it ‘general’ and ‘approachable’ to the common, you need to minimize the dependency on coding skills and data science expertise as much as possible.

 

Microsoft Lobe

Further to make it easy for non-coders/experts to use, Microsoft has recently released a tool called Lobe. Its beta version is released. Lobe was acquired by Microsoft a couple of years back. This was inlined with their vision to make another step towards AI adaption.

Microsoft says Lobe uses

“Open-source machine learning architectures and transfer learning to train custom machine learning models on the user’s own machine”

Lobe gives you an image classification model in few minutes with NO CODE at all

From Lobe –

“Lobe has everything you need to bring your machine learning ideas to life. Just show it examples of what you want it to learn, and it automatically trains a custom machine learning model that can be shipped in your app.”

I thought to try this out myself. Trust me it was fun 🙂

Being a bird photography aspirant I thought its best to use my photographs to classify whether they are birds or not. This is what I did,

Steps

1. Download the Lobe desktop app from https://lobe.ai/

2. Once successfully installed open it up. It has a very simple and intuitive UI. It just needs 5 images to train the model.

lobe front page

3. Import images: import images from the right top corner buttons.

import images to lobe

4. “Label” the image: You can label the images 1 by 1 or first import all of them and then label them.

bird 1 to lobe

Statistics about images/labels will start updating on the left side of the screen.

5. Import some images for the other class. I’m keeping another class where there’s no bird. I call it “No Object”.

river to lobe

6. Repeat #3 – #5 for as many images as you want. Ideally, you should keep a balance between the classes, otherwise, you’ll face a “class imbalance” problem.

You’ll notice that in the background the “Train” is running. This means that in the background the model is getting trained.

repeat images

8. Once training is done, hit the “Play” button. Now it’s like testing your model which you just created. Add new images and see if your model can correctly classify your image. As you upload an image, it’ll try to guess it, label it, and also it’ll show statistics on the left.

9. If you did not correctly predict the class, you can always change/update the label. This way you are telling the model that “hey you did not do well here, correct this”.

The app will note that down and improve its model to predict better. You can do this kind of test and correct the label few times and then you are just done.

lobe prediction

10. Now you can export your model with all the model details with TensorFlow, TensorFlow light, and few more options.

There are other ways to use the tool by clicking images on go. An illustration can be watched on Youtube – Introducing Lobe | Build your first machine learning model in ten minutes.

 

End Notes

This is a fantastic tool for the people who want to do something with AI and Machine Learning but not an expert in data science or programming. Also, this gives access to AI-like things to common people in easy to use way. You can use it for a variety of things for example detection of a person wearing a mask or not. If a person sleeping or not etc.

Microsoft has released a beta version of the tool, which is right now limited to image classification problems. It’ll be interesting to see how it progresses and how it removes the dependency on the complexity of data science and brings it to more commonality.

 

Author

Raja Saurabh Tiwari

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