6 ms·

Darknet – A neural network framework written in C and CUDA

63 points by ZeljkoS 8y ago

nine_k 8y ago
> Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. It apparently lacks any ergonomic scripting language bindings, which makes experimentation harder (than with tensorflow). Or, if it does, it should list them right in the readme.
- freeone3000 8y ago
  It's easier to use than CuDNN directly, and not having a python dependency can be considered a feature.
mr_woozy 8y ago
really, "darknet"? Couldn't have used a better name?
- TomMarius 8y ago
  I think it's a good attempt at redefining a stupid term.
cabernal 8y ago
AlexeyAB's fork [0] has a ton of improvements on the original implementation linked here. [0] https://github.com/AlexeyAB/darknet https://github.com/AlexeyAB/darknet
syntaxing 8y ago
FYI, this is the same person(people?) that came out with the YOLO Objection Detection classfier.I haven't used Darknet before but the Tiny Darknet seems very interesting and I might use that in the future for my small projeccts
esotericn 8y ago
This is the first I've heard of this project. There are some rather majestic words and images on that page. A particular favourite: https://www.youtube.com/watch?v=VOC3huqHrss https://www.youtube.com/watch?v=VOC3huqHrss
therein 8y ago
Looking at the resume, I noticed I've seen his resume shared on HN before as an example of an unconventional looking resume. Anybody remember the resume with the ponies? https://pjreddie.com/static/Redmon%20Resume.pdf https://pjreddie.com/static/Redmon%20Resume.pdf
- gbear605 8y ago
  Here are a couple instances of it being discussed before: https://news.ycombinator.com/item?id=17500616 https://news.ycombinator.com/item?id=17500616 https://news.ycombinator.com/item?id=15060129 https://news.ycombinator.com/item?id=15060129
symlinkk 8y ago
These commit messages, lol.
- jazoom 8y ago
  Not very useful but since it's his own project he can do whatever he likes. It looks like he's having fun, which is great. Except his beehive died. That kinda sucks.
- lszu 8y ago
  Also check out the licenses...
lucb1e 8y ago
That title is very non-descriptive or, at worst, misleading. I thought it would be about an Internet overlay network or something. Repository's description is Convolutional Neural Networks, so perhaps the title could be "Pjreddie/darknet: Convolutional Neural Networks"? (Not sure if pjreddie is supposed to be well-known, there is probably a reason OP added it to the title.)
- sctb 8y ago
  Thanks, we've updated the title from “Pjreddie/darknet”.
petee 8y ago
The License 'stack' made my morning, thank you: "THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN TROUBLE"
- avinassh 8y ago
  The license 'stack' joke because of neural networks?
  - petee 8y ago
    no pun was intended; because there is an actual stack of license files for whatever license you want to pretend applies, as its public domain.
joshvm 8y ago
I've used darknet quite a bit over the last few months. I wouldn't recommend using it for a serious project, unless you happen to need an extremely fast off-the-shelf object detector. The other exception is if you need a fast convnet running on a Pi - Tiny Yolo will do about 1.2FPS with an optimised fork (using nnpack). Other than that you could just buy a Neural Compute Stick. Yolo itself is pretty great - it's very fast and is accurate enough for a lot of things. The original repo isn't really updated, and while AlexyAB's fork is much improved, it's still a pain to use. - If you make mistakes, things fail silently. This is by far the biggest problem. Train/test is difficult to get right because it's very difficult to figure out where exactly you've messed up. - Support for images is arbitrary. Although you can compile with OpenCV, there are internal glob functions which simply ignore certain image types (I had to recompile it with support for TIFF, for example). - Bounding boxes are stored in an awkward format, which is easy to get wrong. It's referenced to the centre of the box, stored as a fraction of the image width. - Logging is very basic. Alexey added a loss graph, but that's about it. If you restart training from a checkpoint, you only get a loss curve from where you restarted. - Retraining on your own data can seem like dark magic. There's a lot of "copy this config file and edit these numbers" and if you get it wrong, you've wasted a day training. If you need to use Yolo, I'd recommend looking at reimplementations in more mature frameworks like pytorch (e.g. https://eavise.gitlab.io/lightnet/ https://eavise.gitlab.io/lightnet/)
wpdev_63 8y ago
It would be great if these AI/neural frameworks targeted opencl so it would be platform agnostic. AMD/Intel/Arm makes so very cost effective GPUs that run everywhere.

63 points by ZeljkoS 8y ago

> Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. It apparently lacks any ergonomic scripting language bindings, which makes experimentation harder (than with tensorflow). Or, if it does, it should list them right in the readme.

freeone3000 8y ago
It's easier to use than CuDNN directly, and not having a python dependency can be considered a feature.

mr_woozy 8y ago

really, "darknet"? Couldn't have used a better name?

TomMarius 8y ago
I think it's a good attempt at redefining a stupid term.

cabernal 8y ago

AlexeyAB's fork [0] has a ton of improvements on the original implementation linked here. [0] https://github.com/AlexeyAB/darknet https://github.com/AlexeyAB/darknet

syntaxing 8y ago

FYI, this is the same person(people?) that came out with the YOLO Objection Detection classfier.I haven't used Darknet before but the Tiny Darknet seems very interesting and I might use that in the future for my small projeccts

esotericn 8y ago

This is the first I've heard of this project. There are some rather majestic words and images on that page. A particular favourite: https://www.youtube.com/watch?v=VOC3huqHrss https://www.youtube.com/watch?v=VOC3huqHrss

therein 8y ago

Looking at the resume, I noticed I've seen his resume shared on HN before as an example of an unconventional looking resume. Anybody remember the resume with the ponies? https://pjreddie.com/static/Redmon%20Resume.pdf https://pjreddie.com/static/Redmon%20Resume.pdf

gbear605 8y ago
Here are a couple instances of it being discussed before: https://news.ycombinator.com/item?id=17500616 https://news.ycombinator.com/item?id=17500616 https://news.ycombinator.com/item?id=15060129 https://news.ycombinator.com/item?id=15060129

symlinkk 8y ago

These commit messages, lol.

jazoom 8y ago
Not very useful but since it's his own project he can do whatever he likes. It looks like he's having fun, which is great. Except his beehive died. That kinda sucks.
lszu 8y ago
Also check out the licenses...

lucb1e 8y ago

That title is very non-descriptive or, at worst, misleading. I thought it would be about an Internet overlay network or something. Repository's description is Convolutional Neural Networks, so perhaps the title could be "Pjreddie/darknet: Convolutional Neural Networks"? (Not sure if pjreddie is supposed to be well-known, there is probably a reason OP added it to the title.)

sctb 8y ago
Thanks, we've updated the title from “Pjreddie/darknet”.

petee 8y ago

The License 'stack' made my morning, thank you: "THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN TROUBLE"

avinassh 8y ago
The license 'stack' joke because of neural networks?
- petee 8y ago
  no pun was intended; because there is an actual stack of license files for whatever license you want to pretend applies, as its public domain.

joshvm 8y ago

I've used darknet quite a bit over the last few months. I wouldn't recommend using it for a serious project, unless you happen to need an extremely fast off-the-shelf object detector. The other exception is if you need a fast convnet running on a Pi - Tiny Yolo will do about 1.2FPS with an optimised fork (using nnpack). Other than that you could just buy a Neural Compute Stick. Yolo itself is pretty great - it's very fast and is accurate enough for a lot of things. The original repo isn't really updated, and while AlexyAB's fork is much improved, it's still a pain to use. - If you make mistakes, things fail silently. This is by far the biggest problem. Train/test is difficult to get right because it's very difficult to figure out where exactly you've messed up. - Support for images is arbitrary. Although you can compile with OpenCV, there are internal glob functions which simply ignore certain image types (I had to recompile it with support for TIFF, for example). - Bounding boxes are stored in an awkward format, which is easy to get wrong. It's referenced to the centre of the box, stored as a fraction of the image width. - Logging is very basic. Alexey added a loss graph, but that's about it. If you restart training from a checkpoint, you only get a loss curve from where you restarted. - Retraining on your own data can seem like dark magic. There's a lot of "copy this config file and edit these numbers" and if you get it wrong, you've wasted a day training. If you need to use Yolo, I'd recommend looking at reimplementations in more mature frameworks like pytorch (e.g. https://eavise.gitlab.io/lightnet/ https://eavise.gitlab.io/lightnet/)

wpdev_63 8y ago

It would be great if these AI/neural frameworks targeted opencl so it would be platform agnostic. AMD/Intel/Arm makes so very cost effective GPUs that run everywhere.