One possible reason https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e i.e the VGG convolutional model wont be good for bounding boxes but only for classification task.

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.

> 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

do yourself a favor and go for the good old yolo, i think it has a tensorflow.js version too.

Just curious. You did align the normalization methods right?

I am not sure what you mean, but if it is normalizing the images and also the box dimensions, plus having them in the right order then yes.

Otherwise maybe not

I am taking a look. But most tensorflow.js versions are either untrainable or too large, so I will probably use Keras and export the models as Layers, If I can manage to. I am still looking for light weight NNs, it's a challenge, so I may ask somewhere for candidates.

train it with either darknet or python then convert the weights if you need to, there are already scripts for everything you need to do, that's why i said YOLO in the first place.

Sorry for the ignorance but wdym by darknet here? All I was planning is to use tensorflow's keras, as tf.js won't make it I think. I started a week ago so I am still digesting things.

you can train yolo on any framework you want and convert the weights later, then load them into your preferred inference framework. ( I'm not sure about js but in python you can load yolo models into opencv as well ). darknet is the original yolo framework which gives you scripts for training the model.

Oh, that helps. I am not sure how true this is though. For example, TF.Keras and TF.SavedModel cant be converted into one another and have different features..Both can be used to predict but only one can be re trained and "tweaked" or extended from JS itself. And I am not sure you can convert Pytorch weights to Keras, but I will investigate. Apparently there is ONX that can be used to do it. I just dont want to train something that can not be converted and loaded into a browser.

What I learnt so far is that Sliding Window, Region of Interest, and Yolo are more like ways to prepare your data, and mostly any CNN could do the job, with more or less precision, I may be wrong. I am following this series https://www.youtube.com/watch?v=XXYG5ZWtjj0&list=PLhhyoLH6Ijfw0TpCTVTNk42NN08H6UvNq&index=2&ab_channel=AladdinPersson

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Think they mean do you apply the same normalization you applied to images during training to the new images you are trying to predict

Yes, I pad them to make them square and resize to 224 using bilinear algorithm, and /255, then the inverse for predicting.

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Try instead of the rescale = 1. /255 to use its own preprocess function and report back

What do you mean? The values were normalized in that way.

I think VGG16 will never work for an object detection task after reading about it. Do you disagee?

It is interesting you commented this. I have spent a day yesterday trying to train some Yolo in python, but all the implementations on github are quite obsolete, apart from Yolov7.

Unless you refer to ultralytics only?

I had serious issues with the whole training of VGG...this fixed it...

give it a spin.

Yes but I dont really know what is your solution ?

I can skip rescaling it, but what then?

Mind to tell what were you using it for?

go for YOLO v3 or YOLO v4. i promise they'll be good enough for you, don't be bothered with version numbers . (if you need lighter models go for tiny versions of v3 and v4).

I did it with tiny yolo v7, using google colab. My point is that there are barely any projects that are usable, unless you found some?

Yes the results were great, I am thinking of writing a little blogpost for others, it is actually quite simple because I found a tutorial in roboflow this time around.

Thanks for your support !

I have found out that VGG can indeed be used with SSD for the same task. Idk exactly what is the general idea but mostly you can combine CNNs with something else and get the bouding box. Pytorch has a SSD-VGG model.

I wonder why pytorch has no yolo implemented that we can just use..

Sorry to be annoying but I thought it was nice to give you some news as well. I was confused as to why there isnt yolo in pytorch, here it is why https://github.com/pytorch/vision/issues/6341

no you're not annoying at all, thanks for the update :)

Another update, I am reading the first yolo paper:

>We also train YOLO using VGG-16. This model is more accurate but also significantly slower than YOLO. It is useful for comparison to other detection systems that rely on VGG-16 but since it is slower than real-time the rest of the paper focuses on our faster models.

Which also explains that my main error was to use VGG16 without a good idea of how to make it understand where the objects are, which is what they did..