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 or --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!

@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.

Custom Vision workflow question regarding Tagging and Training during object detection

Accepted answer

Ramr-msft 17,611 Reputation points

2022-12-26T14:57:49.02+00:00

@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.
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1 person found this answer helpful.
Ryan 21 Reputation points

2022-12-30T21:41:53.81+00:00

I greatly appreciate the super fast response! You are saving me tremendous time by helping me find out which of our flows are simply unnecessary or even incorrect.

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(1) Following up on your answer from my previous post. (You basically already answered this question by stating that there is no difference in the way an image ends up in the tagged folder, thank you.) I wanted confirm that there is no type of "Negative Training" or "Corrective Training" going on after correcting and tagging a Quick Test. A theory had been floated around here that if you Quick Test and correct the AI's suggested results, it learns even more from that "Corrective Step" of NOT accepting the AI tags, and then allowing that image to get to the Tagged bin. Does this exist?

Ryan 21 Reputation points

2022-12-30T21:42:36.347+00:00

(2) This question is regarding the images left in the Predictions tab after a quick test. These would be the images that the user didn't tag, and left there to be able to view results of that iteration.

Are these images in the Predictions tab cut off from everything else, or would they ever be referenced by the AI for training....or anything at all? My theory is that they can be safely left in the predictions tab without affecting current Tagged Images, without affecting Future Iteration Training, and without being pulled into "Tagged Bin" or "Untagged Bin" without the user directly doing so by editing within the Predictions tab.

Is this correct? Can my Quick Tested results images in the Predictions tab sit safely there? No need to remove them or worry that they are affecting future training?

You really are a huge help!

Ramr-msft 17,611 Reputation points

2023-01-02T11:29:42.353+00:00

1) No, correcting the tags on a Quick Test image will not have any special effect on the training process. When you correct the tags on a Quick Test image, you are simply adding or modifying the tags for that specific image. This will not affect the training process in any special way.

The Custom Vision service uses all of the images in the Trained folder, regardless of how they got there, to train the model. The tags on these images are used to teach the model what to look for in the images. The model will not pay special attention to images that were corrected during Quick Test or images that were added directly to the Trained folder.

Ramr-msft 17,611 Reputation points

2023-01-02T11:32:13.597+00:00

2) Yes, you are correct! Images in the Predictions tab are not used for training and will not affect the training process in any way. They are simply a preview of how the model is currently performing on those images. You can leave them in the Predictions tab without affecting the current or future training of the model.

The Custom Vision service only uses the images in the Trained folder for training. These images must be tagged with the objects you want the model to recognize. The tags on these images are used to teach the model what to look for in the images. The model will not pay attention to any images that are left in the Predictions tab.

Ryan 21 Reputation points

2023-01-05T17:17:19.487+00:00

Thanks for all the support! Your answers were exactly what I was looking for....extremely fast as well. Much, much appreciated!!
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Custom Vision workflow question regarding Tagging and Training during object detection

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