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Thanks! I will take a look =)

Use scikit learn mlp classifier if you have to use mlp.

Transfer learning. With the availability of these large models, it will be in demand to adapt them to smaller/use case specific datasets.

Another approach can be cyclegan or pix2pix, both are gan based model. Pix2pix works on paired data and cyclegan works can use unpaired data.

Thanks alot! =)

Search for ‘Neural Style Transfer’.

Some helpful links:

https://www.tensorflow.org/tutorials/generative/style_transfer

https://huggingface.co/spaces/aravinds1811/neural-style-transfer

Ow I see,
I have already done something similar but the results were not convincing.
Maybe I'll try it again using the masks.

Exactly, because you have x_t-1 with some (unknown, data/normalisation dependent) variance and you add noise with variance \beta to get x_t.

For example you use a bubble mask to "cut" out a bubble patch from an image. Cutting can be a mix of rectangular selection + transparency where the mask equals zero.

Then you do some random change (flip, aspect ratio…) to this patch.

Then you take a defect free image, and you randomly choose a location where to paste this defect using the bottle mask. You can paste by simply over-writing on the image, or you can do a linear interpolation between the patch image and the defect-free image so it's smoother.

I assume you have very similar images, since they're from a standardized inspection process. Also the bubbles are a very simple pattern. So this could work quite well.

I was referring mostly to cost and time efficiency.

Thanks for the good ideas, i'll try them out.
What do you mean by the last part? How can I create patches of defects using the segmentation data?
You mean by classical methods?

18 is your Z dimension, if you move it to the third dimension so Bx3x18x90x90 you can apply multiple 3DConv until you reach a 2D representation and after that you apply 2DConv. For example, let's say we apply 2 times 3DConv->3DMaxPooling with kernels (3,1,1) and (2,1,1) you'll end up with an output of BxCx3x90x90, if you then apply a single 3DConv with kernel (3,1,1) you'll have an output of BxCx1x90x90 or simply BxCx90x90 which can then be passed to 2DConv layers. So basically you ask the model to compress the info in your Z dimension before moving to the spatial dimensions. You can also do the two things together by playing with the kernel size of conv layers. That said, integrating this into UNet it's a bit more work than just using a predefined UNet but it is doable, look for 3D+2D Unet for example.

You're right, I'll try to incorporate more representative data.
I'll also try different splits
Thanks

Got it 🙌

Efficient or more cost effective or both?

Helpful insight 🙌

Thanks 🙌

Holy crap, if this works that’s a huge game changer for a problem I’m working on.

Thing is, a lot of these problems are always solvable my Tree (non DL) models if you design your data smartly. But GNNs have so much potential to capture information, especially with the use of context attention to get exactly the right patterns you're seeking.

I fucking love the Internet.

> Any small diversion will be detected as defective.

If this small diversion is smaller than the diversion within your train dataset (+ the noise) then it should not be detected.

Did you try different splits or a CV ?

I see. Typically, when you say "this thing" you're referring to the most recent mention of that thing. So "Here's my article. This article is fabulous." is probably not the structure you want. You really should reference the paper or the researchers you're writing about straight away, if that's what you're doing here. Even after skimming your article, that isn't clear.

It's a cautionary tale about "this" really. A tip that has helped my writing is to accumulate a list of words that I tend to over-use that add nothing and search for them in the editing process. Words like "this, really, just, again," and so on.

Well first it really is a use case for anomaly detection, but I see there's a discussion on it in another comment.

I still don't know if you have (classification) labels, bbox, or masks for the images with defect… if you do not have any bbox or mask, I suggest you try creating mask annotations, as this will increase the "power" of each sample (instead of 1 label per image you get HxW labels for each pixel location).

It should be fast as:

1. You say you do not have many images with defect.
2. It seems the mask for defect-less bottles can be generated with a simple thresholding.
3. Looking at your example you can create a bubble annotation in one click with a proper annotation tool (I can't tell for the other defect)

Then you ca try a simple supervised training. You could have a nice surprise since your picture are taken in a controlled, standardized environment (it's not like you'd have pictures of bottles on a beach or in the jungle).

If not then you will be able to use the segmentation data to create patches of defects that you can use on your defect-free bottle to create new data. It will be much easier than messing around with GANs and might give you good results.

Maybe a conv3d helps better without having to reshape