Training a Darknet/YOLO neural network in less than 90 seconds

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Training a Darknet/YOLO neural network in less than 90 seconds using only 24 images.
- Darknet: github.com/hank-ai/darknet
- DarkHelp: github.com/stephanecharette/D...
- DarkMark: github.com/stephanecharette/D...
- Darknet/YOLO discord server: / discord

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Опубликовано:

28 апр 2024

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Комментарии : 12

@StephaneCharette 2 месяца назад

All of the files (images, videos, annotations, cfg, weights) are available for download here: www.ccoderun.ca/programming/2024-05-01_LegoGears/

@brunoluiz719 3 месяца назад

thanks for the explanations @Stephane Charette!👏👏

@visaocomputacional2246 3 месяца назад

Esse cara domina, me ajudou muito quando eu estudava reconhecimento , obrigado Stephane.

@leo1722467 3 месяца назад

Ele é gente boa mesmo, fez algum projeto mano? Trabalha com isso ou só curiosidade msm?

@visaocomputacional2246 3 месяца назад

@@leo1722467 fiz mano tá nos meus vídeos reconhecimento de armazenamento de fogo, mais depois eu parei, eu tava ou tô ainda até hoje no grupo dele no Discord,aí vi eles comentar sobre uma técnica para girar o retângulo de reconhecimento era augo novo que ainda tava lançando, aí na época eu parei , esperando essas coisas aparecer e não voltei até hoje.

@devwof 20 дней назад

Hey Stephane, first of all thank you for all your materials. These are extremely helpful and to the point. Is OBB (oriented bounding box) feasible with darknet yolo? Are there any forks supporting that by chance? I am aware of yolov5-obb and fairly recently yolov8-obb but these are clearly not darknet as your yolo is.

@oilbender Месяц назад

Hello, thanks for the video. How would this perform on a jetson nano if possible at all given its older version of ubuntu

@StephaneCharette Месяц назад

See this post: www.ccoderun.ca/programming/2021-10-16_darknet_fps/

@ThePandaGuitar 3 месяца назад

What's your hardware configuration?

@StephaneCharette 3 месяца назад

My GPU is a NVIDIA GeForce RTX 3090.

@ckpioo 2 месяца назад

@@StephaneCharette CPU, RAM?

@StephaneCharette 2 месяца назад

@@ckpioo Ubuntu 20.04, 24 GiB vram, 32 GB ram, AMD Ryzen 9 @ 2200 MHz. Doesn't matter, the point is not to actually train in 85 seconds, the point was to show that if your network is limited to a specific situation, you can train in a very short amount of time instead of "days" and with thousands of images.