YOLOv9: How to Train on Custom Dataset from Scratch with Ultralytics
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- Опубликовано: 26 июн 2024
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In this video 📝 we are going to create the whole computer vision training pipeline on a custom dataset. We will take raw images, auto label them with roboflow and export it into a Google Colab notebook. We are then going to train the new YOLOv9 model with Ultralytics in just a few lines of code. Then we download the model and see how to use it in a custom Python script.
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Timestamps:
0:00 Intro
0:42 Upload Dataset
2:33 Auto Label
5:50 Generate & Export Dataser
9:00 Train in Collab
18:20 Inference in Python Script
20:37 Outro
Tags:
#YOLOv9 #objectdetection #computervision 
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Hey. Do you have any idea why YOLOV9-t (the tiny model) weights are not available?
Thank you for this amazing video this is very useful.
Thanks a ton for watching!
@@NicolaiAI can I know how to integrate this to a live video captured from an esp32-cam? Do you have any videos on that?
I am working on FSC 147 dataset and I will combine SAM and YOLO for counting and segmentation tasks, for trial of custom YOLO I was trying it on CarPK dataset and was facing some errors, but your video just released on the right time and I was able to solve ther problems I was facing with trial. The results I acheived were pretty good and therefore my professor agreed to my idea of combining SAM + YOLO for counting and segmentation tasks. I know YOLO can perform both segmentation and counting task, but we want to use SAM for counting, and my idea was a to add a layer of YOLO/ CNN model to accurately predict the objects.
Thank you so much for this.
Papa papa aree q
Hello Harsh, I am also kinda working on a similar project. Right now, I am using only YOLO for both segmentation and counting tasks but I want to use SAM for counting purposes and I really need help on this. Can we please connect somewhere and discuss it?
Can you make video about semantic segmentation?
hello , How to resume YOLOv9 training an after interruption?
how to evaluate the yolo model in google colab notebook
Also after training custom dataset , the best. pt file, when I give any image(I.e things that I didn’t train) it’s still detecting it (I.e it’s detecting a bike as a car) ps I trained only car and not bike.. so I suppose my result image should not be predicted… please send help
Comes down to the dataset you are training on
loving your video! is it possible to just train the model locally and not on google colab?
Yup you can do it locally as well. Exact same code. And it will use the hardware available. Either GPU or CPU directly
Hey!The following error occurred while I was running, what should I do?
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
What is the point of training a yolov9 model if the foundation model can already do almost perfect predicitons? Just knowledge distillation?
Depends on what classes you want to detect. The pre trained model is only able to detect 80 different classes from the coco dataset
@@NicolaiAI Sorry, I meant the foundation model from roboflow, which annotated the cars.
Ohh in that way. Those foundation models are too large to run in real-time and too expensive for the task. They are way too overkill and requires significantly more processing power which is unnecessary @@rololop34
@@NicolaiAI Thank you for answering. I just read on the ultraytics docs that YOLOv8 is approx. 866x faster than SAM-b on CPU.
how about video and live footage
How to use this model results for practical use like autonomous robots
In what way do you want to use it for? Any specific objects?
@@NicolaiAI I have certain types of obstacles and want to apply obstacles avoidance and by also identifying them and want to feed the results obtained from prediction to computer or microprocessor for performing tasks
@Mrsmith0119 definitely check out the yolo world model as well here on my channel
Well even I want to deploy this model on a practical bot !
how to deploy these models into a raspberry pi or any edge device?
Same question i also want to ask
Yeah same here!
Will definitely do it on both raspberry pi and jetson nano. Ultralytics have some nice guides on their documentation but I should definitely cover it since it looks like a lot of people want to see that. Thanks a lot for commenting!
Yeah pls do it
You have written yolov9 in the video and you are training yolov8. 😔 😔
Nope 14:21 it’s yolov9 and training right after. Yolov8 from the code example from Ultralytics and then changed the model name
@@NicolaiAI Thanks for your quick response! You are absolutely right. I just watched the full video, and I apologize for the misunderstanding. I'm planning to run it on Kaggle and would love your assistance with that. Thank you so much! I just subscribed to your channel-keep up the great work!
@HarisKhan-ph9jl thanks a ton!
how to evaluate the yolo model in google colab notebook
how to evaluate the yolo model in google colab notebook