IOS13-SimulateTouch VS yolov5

Heck, even if nobody decides to continue support for this, I thought I'd remind people that this exists (at least up to iOS 14, I'm not sure which versions are supported past that)

I recently installed ZXTouch on my iPhone (SE2, IOS 14.8, Unc0ver) from the Github page (https://github.com/xuan32546/IOS13-SimulateTouch) and i found out that i couldn't open iSecureOS as long as the tweak was installed (it just crashed right after opening).

i would reccomend using [[zxtouch]], the repo might still be down but you can get it from here

get the deb. also join their discord if you run into any issues again.

Is this Chinese tweak related? https://redd.it/hulo30 https://github.com/xuan32546/IOS13-SimulateTouch

This seems to be an open source project: https://github.com/xuan32546/IOS13-SimulateTouch/.

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).