As shown by the example in this post, we collected enough images, uploaded them, and annotated them with regions and labels. Then we created datasets and trained a Deepnet to perform Object Detection. We also evaluated the model and used it to predict new images that detected objects accurately. All of these tasks were done on the Dashboard with a few clicks. This is as accessible as it gets in Machine Learning. And just as our motto suggests, BigML has made Object Detection beautifully simple for everyone. Be sure to visit the release page of BigML Object Detection, where you can find more information and documentation.

As part of our Object Detection release posts, on this post, we would like to showcase the entire application development process from problem identification to model deployment, a seemingly ambitious undertaking. Let me tell you the story of how (and why) I built a plant disease detector web application. You too can build similar applications that will help you in your daily life in just a few hours. If you would like to play with the app, you can find it here and the source code is also available in this repository. A few days ago, I moved to a new home.

Continuing with our Object Detection release blog posts series, today, we'll showcase how to automate the training of the object detection models (and their predictions) that anyone will be able to create in BigML in short order. As discussed in previous posts, BigML already offers classification, regression, and unsupervised learning models (e.g., clustering, anomaly detection). They all accept images as just another input data type usable for model training. In fact, when images are uploaded a new Source is created for each and their corresponding IDs are added to a new Composite Source object with a new image field type. In summary, images can be combined with any other data type and can be assigned one or more labels by using the new label fields.

So far, to showcase BigML's upcoming Object Detection release, we have demonstrated how you can annotate images on the platform, we have covered an example use case to detect cats and dogs and shared how to execute the newly available features by using the BigML Dashboard, as well as another example to build a plant disease detector. In contrast, this installment demonstrates how to perform Object Detection by calling the BigML REST API. Briefly, Object Detection is a supervised learning technique for images that not only shows where an object is in the image, but it also can show where instances of objects from multiple classes are located in the image. Let's jump in and see how we can put it to use programmatically. Before using the API, you must set up your environment variables.

Following our previous posts on Image processing in BigML, the turn has arrived to discuss automation for datasets with images. As BigMLers will already know, BigML offers automation that can be used on the server-side thanks to the WhizzML language, which has been designed especially for Machine Learning tasks, but it also offers client-side bindings for many programming languages. In this post, we'll review the Python bindings approach. From the Machine Learning point of view, each image is a source for many fields, like the light levels in some regions, their color information, or the shapes it contains. In that sense, we need to think of an image as a composed field, just as we could think about other composed fields like text or date-time fields.

BigML's upcoming release on Wednesday, December 15, 2021, will be presenting a new set of Image Processing resources to the BigML platform. In this post, we show you how to build a simple image classifier on the BigML Dashboard. Image classification is a supervised learning technique for images. Image classification models are trained to identify various classes of images and have a tremendous amount of applications as touched on in our prior posts. As such, BigML introduces image data support with the latest Image Processing release.