@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.
2) To increase recognition accuracy when recognizing "a person on a cellphone", which one of the following scenarios would be more accurate:
A) A single tag, used on "people on cellphone" viewed from any and all angles. This would include people facing sideways, frontwards, in between, etc, and ideally this single tag is trained enough to recognize multiple angles
B) Several tags, each specific to the angle that the person is viewed. For example, separate tags for: Facing Forwards, Facing Left, Facing Right, Arms Up, etc with each tag specializing in only subjects in that specific pose.
We felt that the drawback to having several tags would be that some of the tags have too few instances, and may not work as accurately as desired. And thus, missed.
But we are wondering if combining all the various angles into one tag cause it to be slightly less accurate in some way.
Again, any and all thought are appreciated! Thank you!