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I want to create a model which solve a multiclass classification problem. The main concept is:

every picture contain only one object the background is very simple all object is coming from the same object class (exampleÉ knives, hats, shoes, etc.) te model will learn and predict the name of the object. (example: the model learn all type of knives, and when it get an image it will tell us the name of the knife) To be clear her is an example, I have 50 types of knife, and the output of the model has to recognize the correct name of the knife. Knife name could be: Chef's Knife,Heavy Duty Utility Knife,Boning Knife, etc.

To solve this problem, I have started to use annotated, segmented (masked) images (Coocolike dataset) and MASK RCNN model.

As a first step, I got a prediction, but I really don't know if I'm on the right way.

My questions are:

For this problem, Mask RCNN could be the solution, or it is impossible to recognize a tiny difference between two objects from the same class (example Chef's Knife, Heavy Duty Utility Knife)? Have you ever seen almost the same problem/solution, with github repo? Thanks a lot G

ps. This forum is about AI, but I didn't find any place to share my idea with other people. If you know somewhere a good community/forum, please share me.

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Mask RCNN can be a very heavy function for a simple class classification. It is designed to handle multiple object in a single image. So I would suggest you could use much simpler models like VGGnet or Resnet which are backbones of the MaskRCNN. The biggest hurdle you might face is the dataset. If you are trying to capture even small difference between knives and classify them you will need atleast 2000 images per knife type. which might be difficult to get. You may have to do a data augmentation.

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