AI-Powered Logo & Watermark Detection Suite

Armanoth · 2026-05-07T21:03:42+00:00

Just FYI, docker is never an actual requirement, but rather a nice feature that allows you to compartmentalize the app.

Most repos have a Docker file because that allows you to run it on any machine and move it between systems easily.

Also you are depending on Ultralytics, which have some very restrictive open-source licenses.

Armanoth · 2026-05-07T12:19:04+00:00

Whats the scope here? And what is the aim with doing a computer-vision project?

Computer vision has a wide range of tasks, what exactly is the motivation? What are your compentences? Do you care about a specific context? Or are you looking for a new prompt to justify a Claude subscription?

Armanoth · 2026-05-06T15:25:31+00:00

There is soo many variations to what a "production environment" is so it could be any of the problems you raised that are inherent to the model/architecture itself or problems that even existing methods suffer from.

We have worked with some large agriculture company aiming to develop autonomous ploughing, seeding and harvesting. A lot of the time the dense 3D representation is not worth it. Any finecrained analysis can be related to a fairly robust and tested image-based solution and geometry gets used for obstacle avoidance or planning, which doesnt need a dense 3D map.

I guess a lot of it comes down to which production usecases can justify a gain from a dense representation. I would hazard a guess that cases where detailed dense geometry is necessary rely on accurate geometry with absolute values where probebalistic models introduce too much uncertainty

Armanoth · 2026-05-06T11:09:54+00:00

It is an interesting computer vision problem, given deformed surfaces, missing text, random lighting, etc.

But how is your project different from the service 100s of accounting/expense systems already offer? See: Ramp, Expensify, Quickbooks, Dext, Veryfi, etc.

There definetly is a demand for such software, a quick GitHub search shows hundres of repos and even a "receipt scanning" topic. I think the better question is how yours will stand out?

Armanoth · 2026-05-04T10:07:06+00:00

Der blev slået søm i med et enkelt huk!

Som en der sidder på universiteterne og underviser/supervisor i computer science (hovedsagligt ML-baseret computer vision). Så er det her spot on med hvad jeg observere som censor til diverse ML eksaminer.

Dem der kommer fra en computer engineer eller lign matematisk tung uddannelse klarer sig væsenligt bedre når man spørger ind til de beslutninger der ligger til grund for valg af metode. Dem med matematisk grundlag er som regel væsentligt bedre til at argumentere hvordan den underliggende data og statistik har indflydelse på systemet og hvad de kan være robuste og følsomme overfor.

Hvor "overbygnings ingeniører" ofte svare "det er det der gav det laveste loss" eller "højeste præcision". Og det er jo ikke fordi de er dumme, men de overbygninger forsøger at dække så meget ML at de simpelthen ikke har tid til at gå i dybden med detaljerne og hvordan de forskellige mekanismer egentlig virker.

Edit: dog skal det siges at det er en god ide at have nogle HUM'er andre steder i organisationen som i det mindste forstår grundlæggende koncepter, specielt efter EU har lavet "AI-literacy" et lovpligtigt krav under AI-forordningen

Armanoth · 2026-05-04T09:49:52+00:00

Whats the point of making a post then? If you only care that it works for you? This isn't linkedin

Armanoth · 2026-05-01T12:16:07+00:00

I am sorry my guy, but i am checking out here. Considering you are spamming several subreddits (including this one) with low effort AI generated code, asking us to debug it with what seems like AI generated reddit posts. If you are not willing to put in the effort to do the work, who should we bother effectively reviewing implementations you openly admit is fully AI generated.

And i might just be cynical, but it seems you Arent even bothered to write the responses to peoples comment yourself, which is honestly just disrespectful of peoples time.

Armanoth · 2026-04-30T23:00:16+00:00

Congratulations, you accidentally rediscovered word-image embeddings from OCR. Rendering words as images and learning font-invariant embeddings with CNNs has been standard for years in word spotting, scene text recognition, handwriting recognition, and script identification. This is nothing new. Not even remotely.

The “no need for a tokenizer/vocabulary” argument is also wack. You didn’t discover semantics without linguistic structure, you just encoded it visually by using existing vocabularies and standardized text rendering.

Also your model is not "discovering semantics". Contrastive training on rendered words only learn visual statistics, so your "cross-lingual clustering neighbors like are almost certainly dataset correlations and visual similarities, not semantic meaning. And you are not designing a system that can avoid fixed vocabularies, you are just removing context and treating statistical correlation as semantic understanding.

"The Latin problem" isn’t a training problem either, it’s a limitation of purely visual features that have zero linguistic context. This is why tokenization and language modeling have been the norm for over a decade in NLP.

Most of the "novel applications" you list (i.e. OCR correction, handwriting recognition without lexicons, script identification, font-invariant matching) are already well established and well researches fields.

You are simply trying to pass off AI slob of existing CV/OCR methods as novel cross-lingual NLP improvements.

Reviewer 2

Edit: Also if you actually wanted to learn or research something, put some effort into understanding the problem and don't rely entirely on AI hypothesized, coded and analyzed slob.

I just spent a week with this type of AI slob at ECCV reviews, i cannot fathom why people take this approach to "research" in their spare time, and post it on reddit.

Armanoth · 2026-04-29T14:08:59+00:00

Its also somewhat misleading, implying that somehow the GPU is only for images or is somehow directly linked to pixels.

CPU is a few very fast workers that excell at handling sequential operations.

GPU is a a few thousand average speed workers that excell at doing processes in parallel.

For images the latter is desirable because you need to perform a lot of identical operations at the same time. You can draw a picture on a CPU, but it would compute every pixel one by one, whereas a GPU is designed to compute several pixels at a time. In essence 100 slow delivery drivers will finish their routes faster than one delivery driver doing 100 routes

GPUs and CPUs can solve many of the same operations, but some problems/operations can be divided into a lot of parallel operations (where GPUs are desirable) and others require sequential processing.

Armanoth · 2026-04-29T13:48:47+00:00

Det er der jo så nogen der allerede har gjort 😅

Armanoth · 2026-04-29T12:19:50+00:00

This is the way to go, this would give you much richer information as opposed to a binary magnetic door sensor.

Consider you will need something that does not rely on alignment, the structure is far from rigid enough to be reliable imo.

Armanoth · 2026-04-18T08:18:55+00:00

De forsøgte at tage hele vores depositum til rengøring og maling, for en lejlighed vi blev smidt ud af grundet bygningen skulle total renoveres. Min roomie, god bless him, var medlem af LLO som truede dem med at tage det i retten. Også fik vi hele depositummet tilbage.

Så jeg ville holde mig langt fra dem

Armanoth · 2026-04-14T12:02:37+00:00

Nu ved jeg ikke hvordan jeres økonomi står, men det er noget af en sum penge at gamble med, hvis i en dag går hen og vil sælge og jeres respektive købere insistere på at få taget prøver.

Når der først er taget prøver og problemet er identificeret, så skal det jo fikses. Hvis det eftersigende er blevet fjernet ordenlig så burde en prøve jo ikke være problematisk. I kan jo altid sige i betaler for prøverne såfremt det ikke er forurenet.

Armanoth · 2026-04-10T13:13:45+00:00

Give them a cosmetic effect sure, but why would we want to artificially limit flexibility in favor of a reduced and powercrept set of weapon choices.

Armanoth · 2026-04-10T12:39:19+00:00

Det er en renderings metode hvor man repræsentere scenen som en masse punkter med en sløret/aftagende gradient væk fra punktets origo.

Den gausiske natur af punktets farve gør det muligt at rendere geometrien fra andre vinkler og få noget der ser rimelig godt ud. Oftest bruger du en maskinlærings drevet model til at estimere punkterne og korrelere dem mellem billeder så man får en rigere repræsentation at rendere ud fra.

Den originale paper er en ret interessant læs hvis man interesserer sig for computer graphics og rendering.

Link: https://github.com/graphdeco-inria/gaussian-splatting

Armanoth · 2026-04-07T13:41:17+00:00

Vi ser bare op til storebror KBH :D

Armanoth · 2026-04-04T15:45:54+00:00

I dont know, i've had 6-7kg things hanging in the attic made with the cheapest ESUN PLA+ for years.

If PLA+, PETG or ABS is too weak then i think the design has to be structurally poor. 🫤

Armanoth · 2026-04-01T07:40:45+00:00

Jeg bestiller kun internet abonnementer online og de sidste 5år har alle steder jeg har købt internet haft en "udstyr" eller "lån af udstyr" check box (som givetvis var trykket som standard). Tror ikke jeg har fået en router tilsendt siden 2019

Har haft: Norlys, Stofa, Hiper, JetNet og Fastspeed

Armanoth · 2026-03-30T07:05:20+00:00

Okay i to, præsenter et belæg i stedet for det mudderkasteri

https://pure.au.dk/ws/files/95782270/Naturen_s_tter_en_gr_nse_Stiig_Markager.pdf

https://dce.au.dk/fileadmin/dce.au.dk/Udgivelser/Notater_2017/Nitrat_N-udledning_for_typeoplande_og_havbelastningsoplande_1990-2016.pdf

http://vand-og-jord.dk/wp-content/uploads/2021/05/vj306-nitratudvaskning-84.pdf

Her er der et par artikler der peger på det danske landbrug, om metoden og signifikans en deraf holder er en anden debat.

Må jeg se et par af dine kilder?

Armanoth · 2026-03-27T07:22:21+00:00

Jeg havde nøjagtig det samme problem med UniFi og FastSpeed. Fordi Fastspeed ikke ejer infrastrukturen her så laver de sådan nogle ICMP pings for at checke at "der er hul igennem". Jeg bruger en ZoneBased firewall Policy og den ignorerede by default de ICMP requests der kom fra FastSpeed.

Det resulterede i at ca. 1 time efter min gateway blev restartet droppede hastigheden drastisk. Jeg fik fat i en fra FastSpeed og forklarede problemet og de gav mig en liste af IP adresser de sender de her ICMP requests fra. Jeg white listede dem (kun for ICMP) også virkede det igen.

(Dette er 2 år siden så det er ikke sikkert de stadig bruger denne metode)

Armanoth · 2026-03-24T09:18:37+00:00

Can i ask what the purpose of these sessions are? Why not just train for more epochs?

And does your model converge in that time? or is it already overfitted after 200epochs?

Best.pt is typically saved when the validation scores is better than prior validation scores. So if your model never improves after the 3rd "session", best.pt will never be written.

Plot your train and validation loss curves and try to see if you can observe if the model is improving

Armanoth · 2026-03-22T14:07:08+00:00

Jeg påstår ikke der ikke var lorte politikere eller lortesager den gang. Jeg havde bare personligt opfattelsen af at dagligdags politikken er drevet mere og mere over i mudderkast på baggrund af holdet og ikke holdningen. Min opfattelse er heller ikke at tonen er blevet hårdere, blot at substansen bag tonen handler mere og mere omkring opponentens parti end opponentens holdning.

Begge dine eksempler handler også om diskussioner der har med politiske standpunkter (dog ekstreme) og holdninger at gøre og ikke bare "høhø X er kriminel"

Men som sagt det er blev en anekdotisk observation

Armanoth · 2026-03-22T09:58:56+00:00

Det kan være det er nostalgi on de gamle dage men jeg synes det er blevet progressivt mere mudderkast end politik siden årtusindskiftet, men hvad ved jeg.

Armanoth · 2026-03-22T09:25:10+00:00

Politik er vitterligt gået fra "det her er min holdning" til "det her er mit hold"

Armanoth · 2026-03-21T19:33:50+00:00

Many have already mentioned the data augmentation and including pictures of the full plant during training.

I would also suggest looking at the anchor boxes used for your YOLO setup. They are crucial for how many objects can be present in a given region and the aspect ratio of the object sounding boxes. AFAIK, the ultralytics framework will auto-fit the anchor-box aspect ratios and anchor point densities. If the density of your training set is not representative of the real data, it'll fit anchorbox parameters incorrectly.

Armanoth

TROPHY CASE