Extracting the teeny tiny features in images, feeding the features into deep neural networks with number of hidden neuron layers and granting the silicon chips “eyes” to see has become a hot topic today. Computer vision has gone so far from the era of pattern recognition and feature engineering. With the advancement of machine learning algorithms combined with deep learning; understanding the content in the images and using them in real world applications has become a MUST more than a trend.
Recently during the Microsoft Build2017 conference, they announced a handy tool for training a machine learning image classification model to tag or label your own images. Most interesting part of this tool is, it provides an easy to use user interface to upload your own images for training the model.
After training and tuning the model you can use it as a web service. Using the REST API you just have to push the request to the web service and it’ll do the magic for you.
I just did a tiny experiment with this tool by building an image classifier that classifies few famous landmarks.
I’ve the following image set
- Eiffel tower – 6 images
- Great wall – 11 images
- KL tower – 7 images
- Stonehenge – 7 images
- Space Needle – 7 images
- Taj Mahal – 7 images
- Sigiriya – 8 images
Let’s get started!
Go to customvision.ai – just sign in with your mail id and you’ll land onto the “My Projects” page
Fill the name, description and select the domain you going to build the model. Here I’ve selected Landmarks because the images I’m going to use contains landmarks and structural buildings.
I had the images of each landmark in separate folders in my local machine. I uploaded the images category by category. System will detect if you upload duplicate images.
All together 53 images with different tags were uploaded for training.
Training will get few minutes. Optimize the probability threshold to get the best precision and recall. Then get the prediction URL. What you have to do is simply forward a JSON input for the Prediction API.
You can retrain the model by tagging the images used for testing. In a production environment, you can use the user inputs to make the perdition model more accurate. The retrained model will appear as a different iteration. You have the freedom to choose the best iteration that should go live with the API.
You can quickly test how well the model you built us performing. Note that any ML model isn’t giving you 100% accuracy.
If you prefer to do this in a programmatic way, or your application need to do all the training and calling in the backend, just use Custom vision SDK.
The SDK comes pretty handy with training new models and adding labels for the images and training it before publishing the prediction API.
Grab a set of images. Build a classifier or a tagger. Make your clients WOW! 😃