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The task is to detect rotated alphanumeric characters embedded on colored shapes. We will have an aerial view of the object (from a UAS: Unarmed Aerial System), something of this sort:

enter image description here

(One Uppercase alphabet/number per image). We have to report 5 features: Shape, Shape colour, alphanumeric, alphanumeric colour, and alphanumeric orientation.

Right now, I am focusing on just detecting the alphanumeric and the shape.

Using openCV, I have created a sample image by embedding (shape+alphanumeric) image on an aerial view image. (The shapes and alphabets have been rotated by a random angle), something of this kind:

enter image description here

Now, I plan to use a pre-trained YOLOv5 model for detecting the alphanumeric, and shape detection. Basically, I want to perform transfer-learning, i.e fine-tune it for detecting characters and shapes.

I have a script ready that creates the dataset for this purpose. Right now I have one image, but by running a few for loops, I can create many combinations of the (shape+character+aerial view)image to create a dataset, however, I have a couple of related questions to ask before I proceed:

1. What roughly, should be the ideal size of that dataset for performing transfer learning of this sort? A tutorial on yolov5 here:https://docs.ultralytics.com/tutorials/train-custom-datasets/ uses just the 128 images (coco128) for training the pre-trained model again. Is there an adverse effect of using a large number of images for fine-tuning? Right now, I plan to use about 1000 images, although the script is capable of creating much more. Also, we need to consider the fact that the network needs to detect both shapes and characters.

2.If the answer to 1 is that we need small datasets, then to what extent should I consider rotating the texts and shapes? With lesser images, I fear that the network will not gain the ability to learn what "truly" is a $9$ or an $F$.Right now, I have used a gaussian distribution with mean=0, standard deviation=50.

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  • $\begingroup$ You're asking too many questions in the same post. Edit your post to leave only one question. Ask the other questions in their separate post (but don't forget to provide the context there too). $\endgroup$
    – nbro
    Dec 17 '21 at 22:59

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