Custom Vision workflow question regarding Tagging and Training during object detection

Ryan 21 Reputation points

I'm super new to using Custom Vision, specifically the object recognition. I have a question regarding the best workflow and a couple weirdly specific questions. Any input would be appreciated!

When tagging images with the intent to use them for your next Iteration, is there any difference between the following flows:
--Upload via "Add Images", they will be in untagged. Tag them from Untagged
--Quick Test, and then view that image's results in the Predictions tab. Then add/edit/correct all tags here, and when finished it will automatically move to the Tagged bin

Obviously Quick Test shows you some preview results. But besides seeing results, does that second flow (using Quick Test) add any extra umph to the training/tags/process in any way at all?

Does it matter how the images end up in the Trained folder? Is one route more helpful in training than the other route.


Would a good practice be to add a range of images to Quick Test / Predictions tab....and then just leave them there in the Prediction tab (untouched) to observe the results of those images on each future Training Iteration? .....Does each iteration require new Quick Tests to be run or can the existing ones on the Prediction tab just be applied to the a new iteration?

Thanks in advance for any input at all!

Azure Custom Vision
Azure Custom Vision
An Azure artificial intelligence service and end-to-end platform for applying computer vision to specific domains.
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  1. Ramr-msft 10,986 Reputation points

    @Ryan Thanks for the question. There is no difference in the quality of the training data between the two flows you described. The main difference between the two flows is the order in which you perform the steps.

    In the first flow, you first upload the images to the "Untagged" folder and then tag them. In the second flow, you first use the "Quick Test" feature to preview the results and then add/edit/correct the tags for the image.

    Both flows will result in the same end result, which is that the images will be moved to the "Tagged" folder and will be used to train your object recognition model. It does not matter how the images end up in the "Trained" folder, as long as they are properly tagged and have been used to train the model.

    In general, the most important thing is to make sure that your images are properly tagged and that you have a sufficient number of images for each object or class that you want to recognize. This will help ensure that your object recognition model is accurate and performs well.

    1 person found this answer helpful.

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