micrograd VS yolov7

I recently decided to turbo-teach myself basic cuda with a proper project. I really enjoyed Karpathy’s micrograd (https://github.com/karpathy/micrograd), so I extended it with cuda kernels and 2D tensor logic. It’s a bit longer than the original project, but it’s still very readable for anyone wanting to quickly learn about gpu acceleration in practice.

FOr inference, less than 1KLOC of pure, dependency-free C is enough (if you include the tokenizer and command line parsing)[1]. This was a non-obvious fact for me, in principle, you could run a modern LLM 20 years ago with just 1000 lines of code, assuming you're fine with things potentially taking days to run of course.

Training wouldn't be that much harder, Micrograd[2] is 200LOC of pure Python, 1000 lines would probably be enough for training an (extremely slow) LLM. By "extremely slow", I mean that a training run that normally takes hours could probably take dozens of years, but the results would, in principle, be the same.

If you were writing in C instead of Python and used something like Llama CPP's optimization tricks, you could probably get somewhat acceptable training performance in 2 or 3 KLOC. You'd still be off by one or two orders of magnitude when compared to a GPU cluster, but a lot better than naive, loopy Python.

[1] https://github.com/karpathy/llama2.c

[2] https://github.com/karpathy/micrograd

Perhaps they were thinking of https://github.com/karpathy/micrograd

I was (for fun) trying to recreate micrograd in Haskell. The ideia is simple:

Check out this repo I found a few years back when I was looking into understanding pytorch better. It's basically a super tiny autodiff library that only works on scalars. The whole repo is under 200 lines of code, so you can pull up pycharm or whatever and step through the code and see how it all comes together. Or... you know. Just read it, it's not super complicated.

I'm doing an ML apprenticeship [1] these weeks and Karpathy's videos are part of it. We've been deep down into them. I found them excellent. All concepts he illustrates are crystal clear in his mind (even though they are complicated concepts themselves) and that shows in his explanations.

Also, the way he builds up everything is magnificent. Starting from basic python classes, to derivatives and gradient descent, to micrograd [2] and then from a bigram counting model [3] to makemore [4] and nanoGPT [5]

[1]: https://www.foundersandcoders.com/ml

[2]: https://github.com/karpathy/micrograd

[3]: https://github.com/karpathy/randomfun/blob/master/lectures/m...

[4]: https://github.com/karpathy/makemore

[5]: https://github.com/karpathy/nanoGPT

Just published my first crate, rustygrad, a Rust implementation of Andrej Karpathy's micrograd!

I've been trying to reimplement Karpathy's micrograd library in rust as a fun side project.

If you want something off-the-shelf, try YoloV7.

The current dictionary is still in a beta state and has only been trained on 80 words (e.g. 'man', 'dog', 'car', 'keyboard', 'book', etc.; see list of words, see dataset). I used the object detection model Yolov7 (paper, all credits to them).

(Please note, this is a re-post of my original question here, I think this subreddit might be more appropriate for asking this question)At work, we use Unity, we have a project that needs object detection and classification. We decided to use this YOLO7 model (for non-technical reasons, It had to be the exact same model as the company does have pre-trained weights for this exact model). However, Unity only supports ONNX so I exported the model as an ONNX model, using the code provided in the repo:

Github Projects offering pretrained weights and train / run scripts. Example

The only project I found is this one that implements Yolov7

I'm wondering if there's an efficient way to visualize the image augmentations from the Yolov7 hyperparameters list here

yolov7: https://github.com/WongKinYiu/yolov7#performance