Assuming that your deep learning stack uses CUDA: Multi-GPU CUDA is not for the faint of heart, and most likely will requre intense code wrangling on your part. It's not as easy as sticking in another GPU

Your GPUs support the outgoing NVLINK, and using it would make things easier on you.

https://medium.com/gpgpu/multi-gpu-programming-6768eeb42e2c

Renderfarm like Sheepit.

Me too brother. You have the right idea. The two 3070 will excel together with fp16, while the 3080 you’ll want to do batch stuff standalone.

Image comparison, database to recognize patterns. Accuracy next to 90%. Yeah, need to find new ways to work with my Gpus lol.

Highly depends on the task and especially how fast or accurate it needs to be.

Former miner trying to make use of spare GPU hehe?

I really understand the need for hotkeys. I simply underestimated the complexity. Doing that right turned out to be quite complicated.

makesense.ia is certainly the largest one. I just recently started https://github.com/SkalskiP/yolov5js with the aim to make it much easier for frontend developers without computer vision background to use object detection in their projects. Apart from that, I have https://github.com/SkalskiP/ILearnDeepLearning.py which is a repository containing examples related to my blog posts on Medium https://medium.com/@piotr.skalski92.

I can imagine! Any other side projects that you're working on?

Hotkeys are not a major thing, just a nice productivity enhancer when annotating many images with a more extensive ontology.

Soz, should have been more clear - not to perform both tasks in one frame. More from a UI perspective in the editor, you could add the option to change between them or show both at the same time.

That'd lower the confidence scores but relatively, they'll still be just as false-confident.

Hi u/encord_team 👋! Thank you. I am building it as a side project since 2019. And watching it grow gives me tons of pleasure.

We have some hotkeys mechanism, but it was really purely designed - certainly wasn't my best engineering achievement. I will have to rebuild it in the future.

Detection and classification at the same time? What would be the real-life scenario when you would need both?

Hi u/RandomForests92 - Exciting project! The onboarding experience is smooth and the UI makes it easy to get started on a simple annotation project.

I would consider adding hotkeys for the different label types/label operations and the option to do object detection and classification in one view.

Because you forgot to add the "noise" class.

I think someone didn’t understand what you meant and downvoted or downvoted because you didn’t define ‘z’ and ‘x’ and so on, but I know what you mean and you’re correct. This is another way of looking at it that is completely right.

p(x) for all these images under a CIFAR-10 world is basically 0, but your CNN is not computing that or factoring that in and is just assuming the input images are good images, then estimating the probability of airplane vs. bird for these nonsense images given that they are not nonsense images and given that they come from the same pdf as CIFAR-10….. which is a very very false assumption!

Nearly every data-driven approach to regression and purely discriminative classification has this problem, and it’s a problem of trying to extrapolate far outside the domain that you trained/fit the model in. It’s not about anything else.

Your generated images clearly look nothing like CIFAR-10 training images, so it’s not much different than if I fit two Gaussians to data that was Gaussian in 2-D using samples that all fit within the sphere of radius 1, then I send a 2-D feature measurement into my classifier than is a distance 100 from the origin. Any discriminative classifier that doesn’t have a way to detect outliers/anomalies will likely be extremely confident in classifying this 2-D feature as one of the two classes. We would not say that the classifier has a problem not considering “feature quality”, but would say it’s not very sophisticated.

In the real world in critical problems, CNNs aren’t just fed images like this. Smart engineers have ways to detect if an image is likely not in the training distribution and throw a flag to not have confidence in the CNN’s output.

All I meant by nebulous was that he didn't have a concrete idea for what to actually use as visual quality, and you've nicely described how it's actually a very deep inference that we as humans make with our relatively advanced brains.

I did not mean that it it's conceptually something that can't exist. I think we're very much in agreement.

I don't think it's nebulous. We infuse knowledge, bias, prior etc. like physics (in Lagrangian networks) all the time. I was just addressing his last point. There's no analytical solution for quality we can use as labels.

Networks can understand the difference between pretty and ugly semantically with tons of data, and tons of data only.

https://github.com/wgrathwohl/JEM

I am using this EBM with slight modification (during sampling).

Oh ok cool. Is your code anywhere? What kind of energy model? I have experience with other types of deep generative models but actually am just starting to learn about EBMs myself recently.

I intentionally generated "bad" samples by doing few steps of MCMC sampling. I am also able to generate CIFARR10 looking samples.

I think your explanation is convincing.

Thank you.

In addition to other commenter's [good] point about your nebulous "visual quality" idea, a couple other comments on what you're seeing:

1. Frankly, your generative model doesn't seem very good. If your generated samples don't look anything like CIFAR images, I would stop here. Your model's p(x) is clearly very different from CIFAR's p(x).

2. Why are "standard"/discriminative models' confidence scores high? This is a hugely important drawback of discriminative models and one reason why generative models are interesting in the first place. Discriminative models model p(y|x) (class given data), but don't know anything about p(x). Generative models model p(x, y) = p(y|x) p(x); i.e., they generally have access to the prior p(x) and can assess whether an image x can even be understood by the model in the first place. These types of models would (hopefully, if done correctly), give low confidence on "crappy" images.

you are modeling p(z|x) instead of p(x)

See that's the problem. We benefit from eons for evolution to imprint what quality is (i.e. what correlates with real life) the most genetically.

To tell a CNN about quality without using a CNN to analyze is either cyclical or redundant. I'm afraid.

That I don't know.

How do you propose we define quality?

Hi u/DDDqp! YOLOv7 is for sure on my list. Problem is that, as it is right now YOLOv7 does not offer export to tensorflow.js. I actually created the issue by asking if they plan to add that export: https://github.com/WongKinYiu/yolov7/issues/885 No response yet. But I think it is more than possible. I can even work on that export for them. If they would have exported to tfjs, than I most certainly can work on the NPM package.

That's great, but is it possible to have yolov7? The 5th is lacking in comparison. At least in my case the v7 gave better results.