yolov3 VS darknet

Hey. I only have experience using the official repository, and only use Linux. Could you try the solutions here and see if it helps? https://github.com/ultralytics/yolov3/issues/1643

YOLOv3: https://github.com/ultralytics/yolov3

Each image has an XML file associated with it. The XML files have the corresponding labels and bounding boxes, so I can write a script to convert them into this form, and follow this tutorial on training custom data.

YoloV3/4 is C based if that counts: https://github.com/AlexeyAB/darknet

This is all well-known information, see any (and all!) previous discussions when YOLOv5 comes up. For details: https://github.com/AlexeyAB/darknet/issues/5920

2) YOLOv5 is both slower and less precise than YOLOv4. Why use YOLOv5? Source: https://github.com/AlexeyAB/darknet/issues/5920

So my recommendation is you stuck with Darknet/YOLO and use v4 of YOLO. The Darknet framework license is definitely suitable for commercial use: https://github.com/AlexeyAB/darknet/blob/master/LICENSE

Still to long. Here's my favourite license: https://github.com/AlexeyAB/darknet/blob/master/LICENSE

When YOLOv7 was out, I built a web app to test it against the classic YOLOv4 and shared it with my team, then deployed it online to share with the community.

If you're worried about performance, you shouldn't be using YOLOv5 since it is slower (and less accurate!) than YOLOv4. Source: https://github.com/AlexeyAB/darknet/issues/5920

I looked around online but I only found this post discussing a related issue, leading me to think there seems to be some sort of compatibility issue going on here. And I think this is the most recent version of the file I am trying to compile located on the exact same folder where my copy is and when I opened it it shows CUDA 11.1 in line 307.