How to train YOLO-NAS Object Detection on Custom Dataset | step by step Tutorial | Google Colab

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Deci AI has introduced YOLO-NAS, its latest deep learning model that delivers superior real-time object detection capabilities and high performance ready for production. YOLO-NAS stands for “You Only Look Once – Neural Architecture Search,” and it is a game-changer in object detection.

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  1. coder zero
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Комментарии
Автор

Thank you for the video. I have a question regarding the training phase when I train Moodle on my data I don't notice the summary and mAP 0.50-0.95 ? how can I show it?

malikakarmach
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Thank you Sir! <3

Can you show us how to play with transformer values?

mohaliyet
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I have a YOLO NAS model for animal detection. I ran the model for 25 epochs and have got the best.pth weights. I need to add more epochs, to train it more from where i left off. I have read somewhere that YOLO V5 have such an option. Does YOLO NAS have the same option? If so how can i implement it in colab?

PS. It took me 10 - 15 hours to train for 25 epochs. So I am tight on time. I am not sure whether I am doing something wrong, but I am training using A100 GPU in Colab and its taking this much time. Please advice. I have 17.8 GB of data which has around 38790 images, so i guess it makes sense to take that much time?

I tried looking through the YOLO NAS documentation and google searched it, but couldn't get any concrete ideas.

thangdaoxuan
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hi sir in visualisation what is data.yaml file

ilapartijharinath