Object detection models can be trained to identify predefined objects from digital photographs with a certain accuracy level. This model uses Artificial Intelligence (AI) to analyze the images that you upload.
You may have a huge inventory of diverse objects that require manual detection on a daily basis, which might be time-consuming and repetitive. This is where the Object Detection model comes in to automate the process. It identifies objects by comparing the uploaded user-input data to the predefined objects that the model can recognize. Using this model, one can automate business processes like maintaining an inventory, accounting, and more.
Businesses of any size have their own custom objects to be detected. For example, if you're dealing with construction materials, you can upload images of various bricks used and train the model to identify each of them.
Creator enables you to build and train custom object detection models tailored to suit your business needs.
You can also utilize our ready-to-use models to detect objects from a pre-defined list of objects. In ready-to-use models, you can select the application name and form name from the Microservices section and you'll be taken to the form in which a new object detection field will be created.
To help you get started quickly and explore the possibilities of object detection, you can build and train an object detection model using sample pictures, that you can readily use in your Creator applications.
Let's assume that you've built Zylker's Inventory Management app using Creator, and you want to detect various objects to keep an account of the number of items in stock. Manually accounting for a large-scale inventory is very time-consuming.
Let's say this app keeps count of three construction raw material: hollow concrete blocks, AAC blocks, and lintel blocks.
Here's how you would use the Object Detection model in this case:
Training data is the initial dataset that is used by the model to analyze data patterns, make interpretations, and arrive at a learning that helps it make detections. To train an object detection model to recognize your objects, you have to gather sufficient images that contain those objects.
The images that you'll be feeding your object detection model require the following:
You need to gather sufficient images that contain the objects you want the model to detect. After collecting images, you need to create an object folder and specify a name for it. You can then upload the required images one by one or all at once. You can also upload the images stored in your .zip folder. In this case, the .zip folder's name will be applied as the newly-created object folder's name. The images that you collected earlier will now come in handy because you need to upload them to the object detection model.
After adding the training data, you can review the model details such as model name, its type, size and total number of images added per model. You can also view the object details such as object name, trained images, and object size. In case you need to make any modifications, you can go back and make them. Otherwise, you can proceed to train the model.
Before you can actually use your object detection model in your application, you have to train it to perform and produce the expected outcomes as per your business needs. After you've selected and reviewed your object images, click Train to train your model.
Once training your model is complete, you can view the model details, the model's versions and its deployment details, if any. The model is then ready to be published and deployed into your apps.
You can manage your model in the following ways:
After training your model, you can test your model to check and know how your model works and if the training is satisfactory, before publishing and deploying it in your applications. You can upload a suitable test data (object image) and after testing your model, you'll get the predicted outcome along with a confidence score.
The object detection model calculates the confidence score for your trained model based on the detection results of your test dataset. For example: if you've uploaded a image as test data that is similar to your training images, you'll get a higher confidence score of 96. If you get a lower confidence score, refer here to improve your model performance.
After you train your object detection model, you can publish it to make it available to your users. Once the model is deployed in your applications, a new object detection field will be created and you can start detecting objects. Learn how
To use your object detection model in an environment-enabled Creator application, you must have at least one version of that application published in the production environment. After deploying your model in the application, you can filter between different stages of the environment to check which stage the model is deployed in. Learn how
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