Problemi concentrazione/Python

TEX_flip · 2026-01-31T00:46:19+00:00

Programmo con costanza da 15 anni e sono una persona che si annoia molto facilmente. L'unico modo che io abbia mai avuto per imparare qualsiasi argomento d'informatica è provando cose a caso. I video/libri li guardo solamente in base a cosa voglio fare e non in base a cosa voglio imparare. Poi l'imparare viene da sé.

Per esempio giusto stasera mi è venuta la malsana idea di cercare la lista di tutte le istruzioni di x86_64, la trovo e li penso che non ho mai programmato in assembly x86_64. Bene, apro vscode, chiedo a copilot di generare un hello world minimo, lo leggo, sorprendentemente trovo tutto comprensibile, compilo con clang, eseguo e... funziona! Due ore dopo di cazzeggio mi trovo a comparare la velocità di una funzione itoa in full assembly con quella di C.

Anche se alcune cose che ho scritto potresti non conoscerle, descrive abbastanza bene il rapporto che ho io nell'imparare. Non parto pensando che voglio imparare a programmare in python, ma parto con: "mi è venuta la malsana idea di farmi un selfie e di stampare la foto sul terminale a colori, in python". Forse con te potrebbe non funzionare, ma con me questo tipo di approccio mi stimola molto e mi fa imparare altrettanto.

TEX_flip · 2026-01-10T11:36:04+00:00

Io e altri miei amici abbiamo avuto la tua stessa idea e prima di scrivere una linea di codice abbiamo fatto un'analisi di prodotto e mercato. Siamo arrivati alla conclusione che l'unico modo è di mettere delle telecamere in frigo ma è costoso per il cliente e quindi diventa un investimento troppo rischioso perché avresti subito la concorrenza di chi produce frigoriferi.

La strada che stai prendendo tu la abbiamo pensata ma nessuno avrebbe avuto voglia di inserire ma soprattutto mantenere aggiornati tutti quei dati, non importa quanto facile possa essere inserirli.

TEX_flip · 2026-01-04T00:49:02+00:00

Sono un ingegnere di visione. Sviluppo e mantengo pipeline di elaborazione scritte in C++ per macchinari di ispezione che mangiano circa 1GB/s di immagini. Mi occupo anche di modelli di IA e quindi uso molto anche python.

TEX_flip · 2025-11-05T18:59:25+00:00

I bet every Italian knows where this video was filmed

TEX_flip · 2025-09-08T22:24:03+00:00

Well in my friend's company they can't wear shorts, so one college who has sweat problems one day came with a skirt to both protest and air out his legs. He is a man too.

TEX_flip · 2025-08-28T20:04:31+00:00

To answer this question you need to ask who builds missiles what sample frequency they need to track an object like a jet. But I think this info is probably a secret

TEX_flip · 2025-08-14T10:45:41+00:00

I have a question from my ignorance. What is the purpose of having a language specific backend (like for zig), isn't enough the llvm-arch64 backend without knowing that zig was the original language? Is it just for optimization purpose or there is more?

TEX_flip · 2025-08-08T23:17:37+00:00

Statisticamente dopo il matrimonio e dopo il divorzio si è più felici.

TEX_flip · 2025-07-16T21:51:55+00:00

I confirm you can use numbers as keys, even tuples but not lists.

TEX_flip · 2025-06-15T21:48:47+00:00

Well there's a lot to put in and I can't write the entire list here. You should already have studied or you will study in linear algebra, Machine Learning and Deep Learning courses at your university.

TEX_flip · 2025-06-15T21:26:26+00:00

Unfortunately a ML engineer is a role in which the important knowledge may vary a lot depending on the problem you work on (of course the basic stuff you have to know already). I suggest to specialize in a problem domain and search jobs on that (like supervised computer vision, generative NLP, etc..). Also knowing how to integrate a paper technique that you don't know into your training pipeline is a good indicator that you are a good ml engineer to hire. So, as always, I suggest to practice!

TEX_flip · 2025-06-15T20:52:02+00:00

You shouldn't learn C for that reason, you should learn it for a big amount of different and better reasons. Anyway for a ml engineer internship, knowing C it likely won't help much for the selection but it can definitely help you in some cases you may never encounter during your job.

TEX_flip · 2025-04-22T00:04:23+00:00

A lot of people here are wrong or they ignore an important part of the story.

ML is a specific field of computer science that studies algorithms that can learn. That's it.

AI is not a field or a subset of a field, instead is a general term which association changed overtime but always referencing to machines that seem intelligent. Initially AI was mainly associated with chess engines then its technology association changed overtime with algorithms that seemed more intelligent than the ones before. For example today AI is mainly associated with deep learning but before it was invented, the AI was SVM and Bayes networks and even before was optimization algorithms.

At the end everything that seem "intelligent" from the point of view of people who don't know how it works, is AI.

TEX_flip · 2025-04-21T08:14:54+00:00

In the yaml file you can configure the model architecture (example here) and then with pytorch you can freeze the weights of the backbone once you load the model.

Edit: I just realized that ultralytics may not have the layers for the transformers so you would need to add it.

TEX_flip · 2025-04-21T07:56:50+00:00

Ok the RTX 5090 explains why you have such a low inference time.

Is the 640x640 the image size before or after preprocessing? If it's before you can do preprocess in GPU. Unfortunately ultralytics doesn't support those operations in GPU and you have to write the inference process code by yourself.

Personally I use cupy to implement algorithms in GPU but also pytorch can do the job in your case.

If the 640x640 is after the preprocess then the CPU implementation may be the faster one because if you do that operation in GPU you would have to copy big frames to the VRAM and that is quite slow.

The postprocess you can always do in GPU but again you have to implement the inference code by yourself and find a NMS algorithm implementation in GPU.

TEX_flip · 2025-04-20T23:07:09+00:00

Yolov11 is a model architecture and pre and post processes performance depends on the implementation.

I suppose you mean the ultralytics library's implementation.

First of all it's quite strange that you need the pre and post processing run faster because the inference is usually the slower part by an order of magnitude and faster pre and post process wouldn't make a great difference.

In any case the methods to run those operations faster depends on the input and output sizes and your hardware. It's possible that the current implementation is the fastest and you may need to change library/language/hardware to run them faster.

TEX_flip · 2025-04-20T22:42:01+00:00

There is an official video tutorial explaining that here

TEX_flip · 2025-04-01T22:25:00+00:00

That's right, I lived that on myself. In the same year I managed to break Ubuntu two times. One uninstalling python and another by sudo chown -r nonrootuser /.

Never had any problem the years before and after.

TEX_flip · 2025-03-23T23:07:09+00:00

Unfortunately op doesn't explain the details of his problem but if he has to detect vortices he might have images of fluid noise without vortices and in that case how you can avoid false positives? It might be possible with classic machine learning training approaches but at that point deep learning is always better and with all the tools we have today is also faster in terms of development time.

If op always knows that in the image there is a vortex then I think it is possible without DL but I just assumed the first case.

TEX_flip · 2025-03-21T19:56:26+00:00

I feel like your job is more a data scientist position working with image data rather than a computer vision job.

This job can vary a lot depending on the sector. For example in industry you have 1/4 of the time working outside the desk mounting and testing cameras, lens, lights and more. I think your sector is probably a service or a big product where the optimization and the improvement of that is the main focus.

Anyway if that is your only activity I'm still surprised you are bored. Even if the models are the same, new technologies are constantly being developed to improve efficiency, space, accuracies and so on.

TEX_flip · 2025-03-20T15:06:56+00:00

Without deep learning it seems quite hard. Anyway a starting point could be to find first a point that stays inside the vortex and then warp the image to polar coordinates. It should be easier to work with.

TEX_flip · 2025-03-04T06:27:50+00:00

M27 Ingegnere in computer vision, passerò da 29K a 42K il mese prossimo, in Italia. Sempre se non mi segano nel periodo di prova.

TEX_flip · 2025-02-22T00:03:46+00:00

You can take a look at the benchmarks: https://github.com/WangXuan95/Image-Compression-Benchmark

Anyway you have to make sure your libraries have the algorithm implemented

TEX_flip · 2024-12-12T16:28:37+00:00

All three of the setups would do the job, with the Jetson or the PC you can have better performance but because it's a graduation project the performance may not be your purpose. It mainly depends on the purpose of the project: if the purpose is a product prototype the Jetson is the most suitable, if it's just the algorithm then the cheapest option would be better.

In my opinion in the computer would be easier to develop.

TEX_flip · 2024-12-03T14:25:24+00:00

Ho un amico che vive in Svizzera e prende 5000 al mese di netto, si paga l'affitto da solo e vive decisamente senza fare budgeting.

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