DarkMark VS yolov5

Also see DarkMark. For several years it has had support for loading custom Darknet/YOLO weights (not just MSCOCO!) to help annotate more images. https://www.ccoderun.ca/darkmark/Summary.html

You should look at DarkMark. I wrote it specifically to do what you describe. It is an annotation tool that loads the Darknet/YOLO weights, so it can assist in annotating images. I annotate a few images and train, reload DarkMark to annotate some more, train, rinse, lather, repeat.

Disclaimer: I'm the author of DarkHelp (the C++ library for Darknet) and DarkMark (the annotation and project management tool for Darknet).

If using Darknet/YOLO, look up DarkMark which does have support for video, as well as loading existing neural networks to help annotate images (or video frames) faster. Some info on getting started: https://www.ccoderun.ca/programming/darknet\_faq/#how\_to\_get\_started

You can find DarkMark here: https://github.com/stephanecharette/DarkMark

Disclaimer: I'm the author of DarkHelp and DarkMark, and I run the Darknet/YOLO discord.

I use DarkMark's video import functionality to extract video frames. See this screenshot: https://www.ccoderun.ca/darkmark/Summary.html#DarkMarkImportVideoFrames

DarkMark will extract frames from a video (lots of options, either all frames, sequences of frames, random number of frames, png vs jpeg, resize frames, ...) and then will let you annotate them as you normally would. https://github.com/stephanecharette/DarkMark

Ref https://www.youtube.com/watch?v=0GwnxFNfZhM https://github.com/ultralytics/yolov5 https://dev.to/gfstealer666/kaaraich-yolo-alkrithuemainkaartrwcchcchabwatthu-object-detection-3lef https://www.kaggle.com/datasets/devdgohil/the-oxfordiiit-pet-dataset/data

!git clone https://github.com/ultralytics/yolov5.git ## Navigate to the model %cd yolov5/ ## Install requirements !pip install -r requirements.txt ## Download the YOLOv5 model !wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt

Specifically what I did was take the coco128.yaml, added 6 new classes from Dataset A (which have already been converted to YOLO Darknet TXT), from index 0-5 and subsequently adjusted the indices of the other COCO classes. The I proceeded to train and validate on Dataset A for 20 epochs.

I am using the default YOLOv5m6 model here with sahi/yolov5 library for my object detection project. I want to change just some of labels - for example when YOLO detects a human, I want it to label the human as "threat", not "person". Is there any way I can do it just changing some code, or I should train the model from scratch by just changing labels?

You should add them without annotations. Go through this.

You are correct and if you check the firmware, it's yet another famous 3rd party project without attribution, namely https://github.com/ultralytics/yolov5

On one side, being a Linux user for 24 years now, it annoys me that they rip off code and claiming it as theirs again, thus violating licenses, but on the other thanks to k3d's exploit I'm able to tinker more with the machine and if needed do (selective) updates by hand then with a closed source system. It's not just "klipper", with klipper, fluidd and moonraker, it's also ffmpeg and mjpegstreamer. It's gonna be interesting since they also use a project that isn't just GPL, but APGL (in short "If your software gives service online, you have to publish the source code of it and any library that it borrows functions from.") - they use yolov5 (for AI).