This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path.
|100||Introduction to computer vision||Article|
|101||Introduction to IBM Maximo Visual Inspection||Article|
|201||Build and deploy an IBM Maximo Visual Inspection model and use it in an iOS app||Tutorial|
|202||Locate and count items with object detection||Code pattern|
|203||Object tracking in video with OpenCV and Deep Learning||Code pattern|
|301||Validate computer vision deep learning models||Code pattern|
|302||Develop analytical dashboards for AI projects with IBM Maximo Visual Inspection||Code pattern|
|303||Automate visual recognition model training||Code pattern|
|304||Load IBM Maximo Visual Inspection inference results in a dashboard||Code pattern|
|305||Build an object detection model to identify license plates from images of cars||Code pattern|
|306||Glean insights with AI on live camera streams and videos||Code pattern|
Object detection has different uses and different opportunities than image classification. This code pattern demonstrates how to use IBM Maximo Visual Inspection Object Detection to detect and label objects within an image (in this case, Coca-Cola products), based on customized training. You can then easily customize this initial data set example with your own data sets-without writing any code.
Imagine that you’re a supplier of an item (such as a soft drink) and you want to know how many bottles there are on a store’s shelf. You can build an app that helps you do just that. IBM Maximo Visual Inspection uses deep learning to create trained models based on images that you upload and label. You don’t need to write any code to train, deploy, and test a new object detection model. You simply upload the images, use your mouse to label the objects in your images, and then let IBM Maximo Visual Inspection do the learning.
With this pattern, you will use deep learning training to create a model for object detection. With just a few clicks, you can train and deploy the model. After you train and deploy the model, a REST endpoint lets you locate and count items in an image. The code pattern includes an example data set to help you build a Coke bottle detector, but you can use your own examples and detect other objects.
IBM Maximo Visual Inspection presents REST APIs for inference operations. You can use any REST client for object detection with your custom model, and you can use IBM Maximo Visual Inspection UI to test it. This example includes an example Node.js app that demonstrates how to upload an image and then draw the image with labels and bounding boxes around detected objects.
When you have completed this code pattern, you should know how to:
- Create a data set for object detection with IBM Maximo Visual Inspection
- Train and deploy a model based on the data set
- Test the model using REST calls
- Upload the images to create an IBM Maximo Visual Inspection data set.
- Label the objects in the image data set prior to training.
- Train, deploy, and test the model in IBM Maximo Visual Inspection.
- Use a REST client to detect objects in images.
Find the detailed steps for this pattern in the README. Those steps will show you how to:
- Clone the powerai-vision-object-detection GitHub repo.
- Log in to IBM Maximo Visual Inspection.
- Create a new data set for object detection training.
- Create tags for training objects and label the objects.
- Create a DL task.
- Deploy and test the model.
- Run the app.
This code pattern demonstrated how to use IBM Maximo Visual Inspection Object Detection to detect and label objects within an image based on customized training. The code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. To continue the series and learn about more IBM Maximo Visual Inspection features, take a look at the next code pattern, Object tracking in video with OpenCV and Deep Learning.