I am currently attempting to learn ML specifically object detection.

Usually I am a JS developer, but wanted to extend my knowledge and incorporate the two at some point.

I hope there are not any stupid questions here.

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I am looking to create a project that recognises different elements drawn on a piece of paper, for example rectangles and squares of different dimensions.

I am currently having to create the images manually by drawing them myself, I am worried this data will become too bias as I know what I want the end result to be.

I then feed that data into CV2 to add a randomly generate background to the image as the first few runs I processed with TensorFlow seemed to pick up mostly the white areas and gave me false readings.

Once I have the background image I use labelImg.py to draw borders and label the areas which contain the elements.

My data it then ready to be trained below are the results I received:-

(I allowed the training to run through 20,000 steps before stopping and testing)

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As you can see from the table above when testing against the same testing images, the more datasets I introduce the lower the success rate of recognising the boxes becomes.

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Question Time:-

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Is there a relation between the amount of steps the trainer should run multiplied by the amount of data sets provided?

eg.

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Is it possible to over train making the machine only recognise the data sets you have provided?

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Is there a place I can request data sets, I do not mind doing this manually, but for bias reasons, I am wondering if it is possible?

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Is there an optimised configuration file for this particular task I am trying to achieve?

I am currently using the ssd_mobilenet_v2_coco config file with some edits to image and batch size.

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Is there any advice you would offer a ML noob to help progress their knowledge?

I have been watching quite a few videos on YouTube (most notably Gilbert Tanner and Sentdex) and they are getting amazing results with only a small dataset and 20,000 steps.

Also just to note, the loss rate sits under 0.8 whilst training around the 20,000 step mark.

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I am hoping the question above are not too targeted and could also help other people who are starting out with ML.