35 STEPS
1. The first step is to open Cogniac homepage and click Box Detection from the Vision AI Applications. You may notice many different options but for now we will select Box Detection under Vision AI Applications.
2. Select the Name to update the name of the Box Detection application
3. Type the Name of the Box Detection application and Press Enter. When naming the application, it is important to name it something meaningful that directly relates to the application purpose.
4. Click Next blue button, and you will proceed to the Input Subjects page.
5. An Input Subject is simply a group or collection of images/ videos which will be then used by the Application for Training. The Input subject is the media which has not yet been processed by the Box Detection Application.
6. Type Input Subjects name. Again please consider a name that will help you differentiate the Input Subject in the future and will be related with the Subject Of Interest displayed in the image. In current scenario we have chosen it to be animals.
7. Click Create a new subject in the current scenario, named as animals
8. Click Next blue button in the end of the page, so you can proceed to the Output Subjects page
9. The Output Subject of the Box Detection application is where you will find the labeled and sorted images. Usually the Output Subject gets populated with media (images/ videos) after successful Training of the Application.
10. Select the text field, displaying the text Enter a subject name
11. Type Output Subjects name/ s. Again please consider the name, as it should be directly related to the Subject Of Interest displayed in the image. For the Box Detection Application, the Output Subjects can be up to 80
12. Click Create a new subject in the current case scenario, named as cat
13. Type the second Output Subjects name which has to be detected inside the images.
14. Type the second Output Subjects name which has to be detected inside the images. In the current scenario, we have selected 'dog' for the name.
15. Click Create a new subject in our case it is dog
16. Click Next
17. In this final step you will select some of the advanced settings. The Frames Per Second Requirement is related with the Application Model and the images which will be processed. Click the Frames per Second Requirement to change the value.
18. Type Frames per Second Requirement in a numeric value.
19. Click Complete which will close the following window and will open the Application Summary window for you.
20. Click the Input Subject which is always displayed in the beginning of the Application pipeline. In our case it is called 'animals'.
21. Click Upload Media to add the initial Dataset to to the Input Subjects. Once it has been uploaded to the Input Subjects, the Media goes for training by the User.
22. Click select files or select folder to upload the initial Dataset of images/ videos to the Input Subjects of the Box Detection Application
23. Click Next once you have selected the files or the folder containing the Media
24. Click Upload
25. Click on the Application Name in our case it is 'animals-box-detection app'
26. Click No Feedback to Provide Feedback for the Box Detection Application.
27. Select the '+' button to add the detection box and select the Subject Of Interest related with 'Cat' in the current scenario.
28. Click highlight
29. With your mouse, click and drag highlight, then drop it on highlight
29b. Drop
30. Select the second '+' button to add the detection box and select the Subject Of Interest related with 'Dog' in the current scenario.
31. Click highlight
32. With your mouse, click and drag highlight, then drop it on highlight
32b. Drop
33. Click Confirm
34. During providing feedback there are also: Back, Sideline and Skip options.
35. That's it. You're done.
