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trajo123 t1_j8rdzfd wrote

Some general things to try:

  • (more aggressive) data augmentation when training to make you model behave better on other data, not in the dataset
  • if by "the problem of bounding objects" you mean object detection / localization then a single regression head on top a classifier architecture is not a good way of solving this problem, there are specialized architectures for this, e.g. R-CNN, Yolo.
  • If you have to do it with the regression head, then go for at least Resnet50, it should get you better performance across the board, assuming it was pre-trained on a large dataset like ImageNet. Vgg16 is quite small/weak by modern standards.

Why do you need to implement this in JavaScript? Wouldn't it make sense to decouple the model development from the deployment? Get a Pytorch or Tensorflow model working first, then worry about deployment. This way you can access a zoo of pre-trained models - at Hugging Face for instance.

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[deleted] OP t1_j8re6mi wrote

> by "the problem of bounding objects" you mean object detection / localization then a single regression head on top a classifier architecture is not a good way of solving this problem, there

I just replied myself a similar thing in a comment. You are correct,

Indeed I am planning to do it in Keras because there arent implemented models in TF.js and doing it is quite difficult.

I do not think Pytorch models can be easily used in Tensorflow JS afterwards right

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