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smsorin t1_iwygv07 wrote

No, and if there was one you wouldn't want it.

The reason is that with a model, any model, you only get guarantees for inputs that match the training data distribution. If the model has never seen a cogwheel before there's no telling what it will do.

If you find one, there's no promise how well it will do on your task. It might work, might work but only half the time or it might just give you wrong answers. To know this, you will need a dataset with your images and the desired labels. If you have this, you might as well make it a bit larger and train the model on part of it.

You will get better results if you start from a model that already was trained on data similar to your, or start with a pretrained model and tune it on your data.

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PegasusInvasion OP t1_iwyhe5f wrote

Thanks for the explanation.

I actually wanted to use a machine learning model to sort my pictures folder based on the objects in the image.

For example, I have 10 images of cars and 5 images of buildings. I wanted to move images of cars and buildings into separate folders.

Do you have any idea, how I can achieve that?

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sudhanv99 t1_iwyhvfj wrote

it is doable but you have to know what images are in your folder. you can find models trained on coco dataset which can do this. but your images have to be in the coco dataset.

you can write a script which will move your files based on the predictions after that.

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PegasusInvasion OP t1_iwyj8hb wrote

I was trying to avoid having to train on a coco dataset 🫤 Anyway, thanks for your help!

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sudhanv99 t1_iwyjd7e wrote

you dont have to train. there are pretrained models, you can just run inference.

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PegasusInvasion OP t1_iwyjhlh wrote

Sorry I misunderstood what you meant by coco dataset. I get it now. Do you know any good models that I can try?

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