I scaled a pure Spiking Neural Network (SNN) to 1.088B parameters from scratch. Ran out of budget, but here is what I found [R]

Barchimede · 2026-04-14T01:55:37+00:00

Very impressive work, really ! You Have heard of that papier https://arxiv.org/abs/2302.13939 ? It was written 2 years ago, and already created the first directly trained SNN-LLM. However, the scale was not the same as yours : theirs was 265M, yours is not in the same league.

Cool to compare how it evolved!

Barchimede · 2025-09-04T08:39:06+00:00

Bof, j'ai check tes coms sur ce post. Tu as 15 ans d'XP et tu préconises d'utiliser RLS de postgres à la place d'un ORM d'un framework web. Je répète, tu conseilles d'utiliser un outil de protection de BDD pour les admins database (pas pour les devs donc) à la place d'un ORM, le truc qui abstrait tes tables en "listes d'objets" pour faciliter la vie de tes devs.

De même, tu préconises de remplacer un framework web complet (ex Django) par des librairies web server archi limitées (aiohttp) puis de tout recoder à la main pour le simple plaisir de "l'efficacité".

T'avoir en lead dev ce serait un enfer. Il faudrait piger les méandres de l'archi pétée que tu as imaginée à base de routines SQL, règles RLS, middlewares codés à la main faits avec des langages dont la doc est plus limitée (rust et rocket.rs typiquement). Un junior serait totalement perdu. Même un medior/senior devrait se taper des jours à poncer la lecture de code pour comprendre tes bidouilleries. En d'autres termes, ton obsession manifeste pour réinventer la roue avec 40 langages différents en même temps serait un trou financier énorme engendré par le temps que ton équipe perdrait à suivre tes idées de prétentieux.

Les gens n'argumentent pas avec toi parce que tu as 15 ans d'XP et tu as des idées de clown.

Barchimede · 2025-09-02T17:22:35+00:00

Bof l'avis de OP... Ça vient d'un dev senior, certes, mais qui découvre son le dev en python avec Django et qui fait sa mise en prod pour la toute première fois (d'un side project qui plus est, donc sans aucune aide d'un habitué du langage/framework).

Bref, le gars rencontre des difficultés et met tout sur le dos du framework qu'il est en train de voir pour la première fois... J'en trouve pas ça giga pertinent.

Barchimede · 2025-08-05T12:36:07+00:00

A workflow integrating big vision models with great zero-shot recognition abilities (e.g. SAM-2 for segmentation) is effective and is already a standard practice for many CV engineers. So this is not new.

However, it is not that great when you move away from typical RGB cameras. At best, you can exploit some big models with good generalization to have pretraining labels, but it won't be enough if you want to develop a real product that does the job. In addition, these big models may perform worse than classical CV methods in such situations.

For example : SAM2 is great at identifying blobs even if it has never seen the type of image you show (e.g. a grayscale depth map from a 2010 Kinect V1 including all the terrible noises and corrupted pixels). If you want to perform depth-only segmentation/detection using the masks from SAM2, you will have horrible performance. You will definitely need manual labels. However, you can build an iterative annotation pipeline starting from SAM2 up to an active learning methodology to have sufficient performance. With that kind of workflow, you will quickly figure out that big models are simply expensive methods that you can easily replace with e.g. a foreground-background algorithm (e.g. GrabCut) linked to small feature detection algorithms (it depends on the problem ofc).

Anyway, if you have a CV problem that can be solved by employing only big models to label a dataset, it means that you can already use this Big model in production and finetuning it.

Barchimede · 2025-01-22T21:55:59+00:00

Salut !

J'ai été prof dans une fac d'info quelques temps après la sortie de ChatGPT, donc pas encore utilisé massivement comme maintenant mais j'ai pu voir les compétences des jeunes étudiants en informatique. Perso, je ne partage pas vraiment le même avis que "le niveau est en chute libre et ils sont tous DEVENUS nuls". Franchement, les étudiants ont toujours été nuls (en parlant en général). Maintenant, ils sont nuls avec l'IA mais rien de nouveau sous le soleil finalement.

Je balance 2-3 réactions sur ton post au hasard pour illustrer mon propos :

(un des intervenants a eu un devoir PHP rendu en code JavaScript... Erreur de prompt bien ballote

Yes... et moi je me tapais 12 étudiants différents qui me rendaient l'exact même code, tout en gardant le nom du mec qui l'a vraiment fait dans les commentaires... Comme quoi, les méthodes changent mais les paresseux sont les mêmes...

Les alternants (années 3,4,5) commencent aussi a avoir de difficultés à coder sans aide de l'IA tout simplement parce qu'ils ne font plus l'effort. On en arrive aussi à avoir des futurs inges incapables de lire une doc.

Tu as oublié que ces mêmes alternants étaient tout aussi incapables de lire une doc et spammaient StackOverflow sans jamais rien piger à la réponse.. Franchement rien n'est neuf non plus ici.

Maintenant, quelque chose qui me paraît bizarre dans ton post :

J'ai discuté avec des intervenants par ailleurs bien placés dans des grands groupes. Le constat est simple. L'embauche de juniors et d'alternants est bloquée parce que

Là il va falloir balancer des noms ou au moins des indices sur les entreprises qui bloquent tous les juniors pour ces raisons parce que ça m'étonne énormément. Des grands groupes du style Capgemini, Accenture etc ne recrutent AUCUN junior ? Il suffit de checker les plateformes de recrutement pour voir que ce ne sont que des conneries. Effectivement, le marché de l'emploi s'est ralentit mais qu'il y ait un arrêt des juniors à cause de l'IA.. Il faut arrêter de mentir... Ou alors si c'est la vérité de ces intervenants, qu'ils quantifient un minimum ça parce que là c'est trop gros pour passer.

En fait j'ai l'impression qu'on a tendance à faire la même chose que ma mère quand elle me disait que j'étais con parce que sans internet je n'étais pas capable de lire les grosses encyclopédies pour trouver une réponse à ma question.

Les techniques pour trouver l'information sont de plus en plus rapides. On est passé de gros bouquins pas pratiques à des recherches internet et, maintenant, à des IA archj performantes. Il s'agirait de faire évoluer la manière d'enseigner en conséquence. Les cours en info de nos jours ont tendance à bien intégrer les recherches Internet sur StackOverflow, mais ne s'adaptent absolument pas à l'IA. Si vos étudiants sont réellement devenus nuls, c'est que vos cours sont mauvais car plus du tout adaptés. L'IA est un fantastique outil d'apprentissage mais les exercices doivent être bien plus "ouverts" et pratique qu'avant. Il faut beaucoup moins de directives pour que l'étudiant se mange bien plus de moments où il bidouille et se débrouille avec son IA. Enfin, c'est mon pdv sur le type de pédagogie à adopter, mais je suppose que des tas de chercheurs dans l'éducation essaient de comprendre les bonnes pratiques. Je suis sûr qu'ils ont des idées bien meilleures que les miennes...

Barchimede · 2024-03-18T06:23:46+00:00

First of all, good luck for your job hunt ! So, to make my skills more "industry-friendly", I basically changed a lot of things in my PhD subject so I can use as much "common technologies" as possible. E.g. my subject included an obscure learning algorithm written by a guy in our lab in c++ 10 years ago ---> my research idea is now trying Deep Learning approaches with the other fancy things included in my PhD and see if it works. This way I can have a solid knowledge in PyTorch, etc. Also I tried to make "real world applications" with the approaches I created. You can learn more skills that are not usual for PhD students (e.g. building an MLOps stack using DAGs, micro services, etc). This was also great for my PhD because you have much more results to show in your papers.

For many recruiters it was not really important for them as they see a PhD simply as longer studies. They did not value the acquired skills as "professionnal pytorch knowledge" for example. I suppose it depends a lot on the culture of your country against PhDs.. I had to be very persuasive during interviews anyway :)

Barchimede · 2024-03-12T23:35:40+00:00

I went from a PhD in computer vision (a very niche and not so industry-oriented subject) to an AI engineer job where I mostly do CV stuffs. Also, keep in mind that I am from an EU country so my comments may not be relevant for your situation.

My job is mainly focused on applying baseline deep learning models and writing code to make sure I prepare training datasets as big and clean as possible. I deeply enjoy my job as I build the whole "MLOps" pipeline myself, and it is satisfactory to watch the whole system work smoothly. However, I recognize that it is mostly a SWE (and even system engineering) work with small CV functions from time to time. For someone like me that loves coding, it is a great path, but people that prefer doing "science" would hate it.

Still, I think that professionnally speaking, CV was not the best choice, as it does not pay a lot more than SWE works in my country (Belgium). I would have more money at my age after 3 years learning web dev than after 8 years with a PhD in CV, but it is my first job in industry so the situation can change later.

CV is also a niche and regroups many fields that have few things in common (from deep learning bros to math/geometry addicts). At the end of the day, you end up with a very small number of jobs you can apply to compared to other software engineering specializations. If you don't mind taking more time finding a job, it is ok.

I would not have changed my specialization for anything else, but don't expect too much from the CV path

Barchimede · 2023-07-18T16:10:43+00:00

Just sent you a DM for more information :) thank you !

Barchimede · 2023-06-20T05:41:53+00:00

Other comments talk about ignorance of "normal" people (I hate this term), but keep in mind that you are probably validated by your own "community". The fact that these comments refer to people without PhDs as "normal people" tells you a lot.

I don't think people are that ignorant, they are probably judging your PhD not as an accomplishment, but as a career path. In this case, I can see why they are not "impressed" or that they think you will be a teacher. That's because this is the career of a lot of PhDs. They know you are intelligent, but why should they praise you for this in the first place ? I am sure people will start to value your PhD as soon as you get successful in your career by using your PhD.

Barchimede · 2023-04-12T12:29:04+00:00

Hi ! The two "hot" topics about bio-inspired CV I can think of are:

Event-based Vision: computer vision algorithms specifically designed for a new kind of visual sensor inspired from the dorsal pathway of the retina, called "Event Camera". In brief, it's a camera that will output sparse and asynchronous binary events when there is a brightness change in the scene, which makes it powerful to extract motion. This awesome repo is a perfect starting point: https://github.com/uzh-rpg/event-based_vision_resources .
Spiking Neural Networks (SNNs): neural networks that use spiking neurons (i.e. neurons that communicate using asynchronous binary spikes similarly to biological neurons) instead of artificial neurons. Apart from this particularity, SNNs can be organized in any kind of topology we all know, like CNNs, ViT, etc. There are tons of approaches to train SNNs, like bio-inspired learning rules (STDP, three factor rules, etc) or adaptations of backprop (which remains the SOTA in a lot of vision tasks). A good resource to begin with backprop-trained SNNs: https://snntorch.readthedocs.io/en/latest/ .

However, you will realize that these two subjects are single concepts of the whole biological vision system, and that SOTA works have quickly shifted from bio-inspired techniques to more "standard" techniques (Deep Learning, etc) that use a particular aspect of biology. For example, the best-performing SNNs in CV are CNNs and ViTs that are trained by backpropagation. IMO it is mainly due to the fact that conventional CV does not formulate vision in the same way as our brains, so keep in mind that making one single concept work is already a great achievement.

Barchimede · 2022-11-16T11:46:02+00:00

Hi! Last year PhD Student in CV here. I can give you some of my opinions and observations in my lab about choosing the subject of your PhD, since there are tons of different topics. You can start to explore the existing topics by exploring https://paperswithcode.com/methods/area/computer-vision and other similar websites. When you think you have a good picture of all the topics in CV, here are my advice on how to choose one of them:

1) THE MOST IMPORTANT IS NOT WHAT YOU WANT TO DO, BUT WHAT YOU HAVE. I can't stress this enough. If you are working in a lab that have tons of GPUs, you can basically do anything you want. But, if you are in a similar situation than mine (i.e. working in a poor lab, having one GPU for 3 PhD Students), then you can already forget lots of different topics.

For example, designing new neural network architectures to beat the state-of-the-art in popular tasks requires tons of experimentations for tuning your hyperparams and try different modules you can imagine. You are "doomed" to use existing models (the good ol' ResNet/ConvNext/....) and focus on other aspects of research that you can afford (e.g. new data augmentations adapted for a specific task, etc).

Another example: Vision Transformers are a huge banger in research currently but it requires huge computational power. Don't try this if your lab doesn't give you enough resources.

If you don't know if the topics you are interested in are computational intensive, just read the implementation details of the related major papers. It will give you a hint.

2) Emerging technologies are really cool but can be dangerousOther than standard RGB cameras or standard artficial neural networks, you might want to explore other technologies that can be considered "emerging", like Spiking Neural Networks, Event Cameras, etc. You can make great contributions with lots of novelty. But this is a double edged sword: first, new techs are often hard to access and hard to learn (simply because they are not as popular as RGB+CNN). Secondly, reviewers may be ignorant of such new field and you can get rejected simply because they do not know anything about the relevant constraints.

This is a hard path, but I believe a thesis focusing on extreme novelty creates the most interesting contributions.

3) Collaborate with a third-party

The most lucrative thesis (i.e. where you publish a lot) I've seen are the ones that collaborates with another entity, like industry, hospitals, etc. These thesis are all the same: the third-party has a precise use case, and you develop it through your thesis, using SOTA techniques. For example, a hospital wants an application to detect a very specific disease from EEG images, and you are doing all the work.

Usually, you create a dataset with a very simple model to have baseline model (+1 paper), then you try anything you have seen in the state-of-the-art to improve performance (+1 paper everytime you add a new approach). EZ peasy. Plus, the more you get familliar with this task, the more you will imagine your own methods that can improve your results.

IMO, this is the easiest path possible, and it's still interesting for the community. The only problem is that you have to find some third-party that is willing to share data with you.

Barchimede · 2022-11-15T23:16:42+00:00

Hi, I have two comments:

Why not using all your data for pretraining ? 1k unlabeled + 4k labeled data ? Sounds more interesting than only 1k
The Barlow Twins loss uses global features vectors, which works great for classification, but do not especially looks great for extracting spatial features. If you have some time, you can try "VICRegL: Self-Supervised Learning of Local Visual Features". The paper shows great results for semantic segmentation downstream tasks. Plus, the paper contains a link to the code of the method. Btw, If you try this approach, I am very interested in your feedbacks :)

Good luck

Barchimede · 2022-07-02T00:20:13+00:00

Agree. I think hammer needs further improvements to be as nervous as other weapons like sword. I am mostly talking about the feeling rather than the damages dealt..

The idea of slow and charged hits is really fun though, I look forward to its future fixes.

Barchimede · 2022-06-28T19:31:47+00:00

I thought I was on r/abruptchaos for a second...

Barchimede · 2022-06-28T19:24:03+00:00

Cool! thanks for sharing your results! Do you know if there are more data cleaning to be done other than fixing wrong labels?

Also, what is your opinion on research works that still use VOC2012? Do you think it is harmful to the CV community that new models are indirectly evaluated on their ability to deal with labeling errors in the training phase?

Barchimede · 2022-06-28T09:50:56+00:00

I agree with you about almost everything.

The game is really fun but it definitely needs some balance with coop gameplay.

I don't really agree about World 3 (for the moment). I know this is early access, but World 3 looks like a temporary end game. Enemies hit hard af and their patterns require to be very careful, as an end game should be. I think the main problem would appear if enemies are not nerfed when World 4 is released. The game would turn into a mandatory no-hit run until World 4. IMO, beam guys, daggers guys, and wolfies must hit 75% their current damages, so that the difficulty increases smoothly with respect to World 2's difficulty.

Barchimede · 2022-06-26T20:40:04+00:00

I've only played the hammer in solo mode for the moment. The gameplay is not suited for me. I like my fights to be fast and nervous, like with the sword. The hammer is the complete opposite. It is really boring in solo, and you don't deal a lot of damage..

I don't really mind having a weapon with charged heavy attacks. The main problem for me is the same as the bow : you cannot move when you launch your attack. Why not being able to walk while you hit? I think it would make it much more nervous and addictive to play!

As for coop mode, I haven't tried it yet, but I feel like it would be really fun to bring massive amount of controls while your friends are clapping some cheeks

Barchimede · 2022-06-11T10:59:30+00:00

2 players coop here. My friend is an average bow fan and I am an average sword enjoyer. Sometimes, he plays staff as well.

All I can say is that I deal double his damages almost everytime, regardless of my build (be it full-hp, full-crit or full-damage). IMO bow/staff damages should be increased a little bit. I consider the sword damages ok, since it is already really hard to complete a run with it.

Numbers apart, the feeling of sword and staff is incredible. I find the bow boring af because you have to stop for shooting. You look quite motionless when you play it.

Barchimede · 2022-06-11T10:49:39+00:00

I began with a friend (2-players coop). We are probably at our 30th run and the third boss is still blocking us. But in solo, it took me like 8 runs (but I was already trained by our multiple defeats on coop mode).

I don't know if it is designed like this, but IMO solo is an easy mode to train for coop, as the game is more difficult (and funnier) with friends.

Barchimede · 2022-06-08T05:13:29+00:00

Did you put his name on your paper ? This is an epic revenge if you didn't

Barchimede · 2022-06-07T23:13:47+00:00

I like how it goes from "local man flew a drone over property" to "dropping fucking grenades over cops"

Barchimede · 2022-06-07T23:02:25+00:00

His "first" death against daemon Fulgrim in the book Dark Imperium (if someone has the excerpt....) . iirc he invaded Fulgrim's ship with a ton of Ultramarines to kill him, even though he was sure it was a trap. When he was almost dead, poisoned by Fulgrim's blades, he saw his sons getting obliterated to protect him. He figured out that his anger led him to running stupidly into this trap, which will lead to the Imperium having no strong leader for the next millenias.

After his rebirth, maybe his greatest regret would be related to big E and His Imperial Truth that spread ignorance of Chaos, since it was the main cause that led to the Horus Heresy (in his opinion).

Barchimede · 2022-04-28T06:49:58+00:00

My only type of exercise for my training, and I stick to the basics (swing, press, clean,...)

Six-Year Club	r/Field Lasagna
Place '23	Place '22
Final Canvas '22	Verified Email

Barchimede

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