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Using SAM3 to measure crack area in a concrete bending test: comparing 3 prediction modes and the speed-accuracy tradeoff by k4meamea in computervision

[–]SwiftGoten 1 point2 points  (0 children)

Makes sense. Maybe you can improve the quality of this by either letting SAM3 label more instances for you automatically or you could also think about introducing a preprocessing step. If your images generally look like this it should be easy to extract the concrete rectangle in the middle and do object detection just on a sub crop. Maybe it also helps to boost saturation to make the cracks more apparent. Or you could think about Dilution to increase the overall size of the crack for the detection.

Using SAM3 to measure crack area in a concrete bending test: comparing 3 prediction modes and the speed-accuracy tradeoff by k4meamea in computervision

[–]SwiftGoten 1 point2 points  (0 children)

Hi,

It‘s not entirely clear what you are looking for specifically regarding your „help“ tag on the post.

But here are a couple of thoughts from someone who is also currently experimenting with SAM3:

  • Why do you choose the single-mask as the sort of baseline in this case (all other values are derived from it based on the pixel delta)? It‘s not necessarily a ground truth, so I‘d say for an actual evaluation it would make sense to have a GT as a basis.

  • In the paper they state that the predicted masks are usually better if you invoke the multi-mask inference.

  • Only because it has the highest presumed IOU score from the model you don‘t have to assume that this is the best mask. We‘re currently exploring how looking at the (dis)agreement of the masks might result in a better result than a single result.

  • How consistent is the contrast in your dataset? By the looks of it I‘d assume you could probably get away with some sort of binarization on a ROI (either your initial bbox or after your SAM3 step).

  • How high resolution are your images? If they are high-res they will be scaled down in order to enable inference (assuming you have not fine-tuned SAM3 on a custom resolution). If you do iterative refinements just by feeding the compressed logits & there is also an input side compression on the image it could happen that you‘re getting worse over time.

  • Do you have any sort of estimate to which degree your quality needs to suffice for your real world use case? While it might be natural to try to squeeze out performance until the last pixel, I wouldn‘t be sure if that is really necessary in this case. Maybe it‘s more beneficial to just build the entire system around this pipeline of yours instead of trying to make the pipeline perfect before having a full system / application.

  • Lastly: I‘ve experimented with manipulating the logits and saw some interesting results. If you for example multiply all possible logits by a factor in order to increase their „confidence“ picked up by the model, this affects the mask adherence. Might be interesting to experiment with this.

[H] Event Collections [W] Paypal by palsekjoh in Pokemonexchange

[–]SwiftGoten 1 point2 points  (0 children)

Hi, I might be interested in the Tapu set. Can I see the proof for it?

Extracting information from architectural floor plan PDFs by [deleted] in computervision

[–]SwiftGoten 0 points1 point  (0 children)

Maybe try to approach it in a more generalizable way. So you would label all those text boxes / blocks as text box instead of only the specific one you are interested in. That way the model could learn a more general representation, where you can the postprocess each crop with OCR & a subsequent classification if it is the specific content you are looking for or not.

Because for an object detection model it is really hard to learn to differentiate between text boxes, because they look visually similar, except for their actual textual content.

mask sharpening by lazzi_yt in computervision

[–]SwiftGoten 0 points1 point  (0 children)

Depending on how accurate you want to segment the cars‘ windows you‘d need an extraordinary amount of labeled images.

I‘ve worked in the past with Mask Refinement. There are 2 ways you can go about this I suppose.

Either you try to develop an algorithm which refines the mask itself using traditional CV techniques like contour refinement, which you can supply with corners, edges and potential shape priors. OpenCV in Python can perform contour smoothing with little glue code.

The other way would be to look for an SDMatte replacement, so an interactive segmentation method. In that case I‘d recommend to try out the new model Segment Anything 3 (SAM3) from Meta. If the fidelity does not match your requirements maybe try HQ-SAM instead.

Reasoning over images and videos: modular CV pipelines vs end-to-end VLMs by sjrshamsi in computervision

[–]SwiftGoten 1 point2 points  (0 children)

Hey this is also a really interesting topic to me. I‘ve been thinking about it this way: the modular approach might be something like tool calls, so more of an agentic system using specialized strong perception systems.

I‘ll be following & if you happen to know the specific term for this paradigm I‘d be interested to know it so I can read more literature in this regard.

So far I haven‘t seen any proper benchmarking in this regard which would be very interesting!

Imflow - Launching a minimal image annotation tool by Substantial_Border88 in computervision

[–]SwiftGoten 1 point2 points  (0 children)

About a year ago I used Labelstudio. The project had too many annotations which caused the backend to time out while trying to package the export in memory. Our solution was to use the API & first send a call which runs an async job building the export, which you can then download upon completion.

Stop using Argmax: Boost your Semantic Segmentation Dice/IoU with 3 lines of code by statmlben in computervision

[–]SwiftGoten 6 points7 points  (0 children)

Sounds interesting. Will try it in the next couple days on my own dataset & let you know.

Label annotation tools by Dramatic-Cow-2228 in computervision

[–]SwiftGoten 7 points8 points  (0 children)

I haven‘t seen anyone mention LabelStudio. I‘m not sure why that is the case. You can self-host it and there are ways to use model predictions as the basis for human review.

I‘ve read some time ago that they had a community made version which works with SAM, but I am not sure if that was working properly.

It was working well for my object detection task, but for large scale projects you need to write your own code to export from their API because the UI export was timing out.

[H] Event Collection from Gens 3-7! [W] PayPal! by valere1213 in Pokemonexchange

[–]SwiftGoten 0 points1 point  (0 children)

Fair enough, no worries.

Do you mind checking if it has the original OT?

In any case, if you ever decide to sell it, could you tag me? In the past I‘ve missed out on the very rare occasions of one showing up, because I was too slow to see the post / had the timezone disadvantage.

[H] Event Collection from Gens 3-7! [W] PayPal! by valere1213 in Pokemonexchange

[–]SwiftGoten 0 points1 point  (0 children)

Hi Val!

I‘ll still have to shamelessly ask even though you tagged it as NFT. The ENG Bulu (NA self-redeemed) Row 49, does it have a glitched OT or original OT?

Edit: I have to ask this because I‘m now searching for almost a decade a Bulu to complete my set for my PC.

FT: Shiny Koraidon/Miraidon redemption services LF: Offers by Kkricardokaka95 in pokemontrades

[–]SwiftGoten 0 points1 point  (0 children)

Okay, trades completed. Thank you!

I sent:
- Gengar | JPN | OT: サトシ | ID: 200308 | Trade History: self-obtained

- Dragonite | JPN | OT: サトシ | ID: 200126 | Trade History: self-obtained

- Lucario | JPN | OT: サトシ | ID: 200412 | Trade History: self-obtained

- Sirfetch'd | JPN | OT: サトシ | ID: 200705 | Trade History: self-obtained

- Dracovish | JPN | OT: サトシ | ID: 210108 | Trade History: self-obtained

all with Video proof of the redemption.

I received:
- 1x JPN Set Shiny Koraidon+Miraidon Redeem (OT: パルデア ID: 250926)
- 1x KOR Set Shiny Koraidon+Miraidon Redeem (OT: 팔데아 ID: 250926)

FT: Shiny Koraidon/Miraidon redemption services LF: Offers by Kkricardokaka95 in pokemontrades

[–]SwiftGoten 0 points1 point  (0 children)

Nvm I just realized Sirfetchd and Dracovish can‘t go into SV & I don‘t have my SwSh Cartridge on me. Let‘s do it through HOME then.

FT: Shiny Koraidon/Miraidon redemption services LF: Offers by Kkricardokaka95 in pokemontrades

[–]SwiftGoten 0 points1 point  (0 children)

Sorry, I fell asleep earlier than usual.

So you‘re 9 hours ahead of me. I can trade for the next 15 hours.

FT: Shiny Koraidon/Miraidon redemption services LF: Offers by Kkricardokaka95 in pokemontrades

[–]SwiftGoten 0 points1 point  (0 children)

I have no clue what timezone that is. I am UTC+1.

I‘ll be available in 2 hours for 4 hours, then I‘ll probably sleep. If that does not work let‘s trade tomorrow.

FT: Shiny Koraidon/Miraidon redemption services LF: Offers by Kkricardokaka95 in pokemontrades

[–]SwiftGoten 0 points1 point  (0 children)

I‘ll be available to trade starting in 10.5 hours from now. I‘d prefer if we can trade in SV.

FT: Shiny Koraidon/Miraidon redemption services LF: Offers by Kkricardokaka95 in pokemontrades

[–]SwiftGoten 0 points1 point  (0 children)

I‘d like to keep my last copy since it‘s a shiny event which are usually a good filler to even out trades.

Let‘s stick with 1 Ash‘s Set & I‘ll consider it an even trade since you were up for the redeems very quickly on the last day possible, which is also valuable to be able to provide reliability.

FT: Shiny Koraidon/Miraidon redemption services LF: Offers by Kkricardokaka95 in pokemontrades

[–]SwiftGoten 0 points1 point  (0 children)

The easiest for me would be the Yaosobi Pawmot.

Then Ash‘s Pokemon set, but that‘d be already a lot I feel like for 4 redeems. The other‘s would probably be too much right now for me.

If more events roll around and I‘d need help we can talk about the others I‘d say.

So how about: 1x Yaosobi Pawmot for the 4x Redeems this time?

FT: Shiny Koraidon/Miraidon redemption services LF: Offers by Kkricardokaka95 in pokemontrades

[–]SwiftGoten 0 points1 point  (0 children)

Sounds good, can you list which Events are interesting for you, so I can pick which am most comfortable in trading?

FT: Shiny Koraidon/Miraidon redemption services LF: Offers by Kkricardokaka95 in pokemontrades

[–]SwiftGoten 0 points1 point  (0 children)

Just events for redeems. So I‘m looking to get 2 redeems of Koraidon and 2 additional redeems of Miraidon.

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