The reason for the slow performance with Yolo-nas is because your running inference using their .predict function. Their prediction functions are slow and not meant to be ran in a production system. You have to export the model into into something like onnx format to get the actual speeds. Once you do that, it should be much faster.
@@NicolaiAI Thats interesting.. I'm starting to doubt their claims that its faster. But looking back at their github, they seem to claim only the 8bit quant models are faster, so maybe they aren't lying? I'll have to try the 8 bit
Thank you for your awesome tutorial. Do these models (YOLOv8 & YOLO-NAS) also work on Android? Please share your experience if you have tried it to deploy on Android.
Hey Nic. This video is amazing.i have a question for you. Can you help me giving the approach , how to build a helmet detection model for motorbikes and a seat belt detector for cars and other vehicles as a single project? Kindly take the pain to share the right approach and models to use for that.
I did a comparison between yolo and detr in a limited datasets and found yolo is a little better even though both of them results are far from satisfaction. Our training datasets are only around 200.
I'm actually surprised that YOLO only did a little better. While transformer models tend to outperform pure CNNs, they are also know to be very data hungry as well.
Love your content but I'm surprised that you didn't use more advanced comparators in the pros and cons like licenses, label format to train on custom data, ... it could have been more informative IMHO
Thanks a lot for watching a the feedback! This was just a quick overview and to more to see how you can run the two models easily. We are definitely going to cover way more of all of that in the future. Stay tuned!
i tried yolov8 for sometime, and when i want to try tuning hyperparameters using raytune, it shows an error, even though i followed the steps provided by Ultralytics, can you make a video about tuning yolov8 hyperparameters using raytune?
hello, really nice tutorial! I created a custom yolo detection with yolov8m as a base, but when I run it, as soon as the speed of the objects increases, the algorithm loses track of me and starts to "jerk" the video output. Does anyone have an idea how to fix this? (yolov8m algorithm trained on 300 custom images) Thanks so much to anyone who can help me!!
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The reason for the slow performance with Yolo-nas is because your running inference using their .predict function. Their prediction functions are slow and not meant to be ran in a production system. You have to export the model into into something like onnx format to get the actual speeds. Once you do that, it should be much faster.
Yup I know, but even in onnx it gets smoked by yolov8
@@NicolaiAI Thats interesting.. I'm starting to doubt their claims that its faster. But looking back at their github, they seem to claim only the 8bit quant models are faster, so maybe they aren't lying? I'll have to try the 8 bit
Thank you for the video! looking forward to watch your video about how to improve YoloNAS inference speed!
Thanks a lot for watching! Have u been trying it out urself?
@@NicolaiAI I tried YoloNAS on my webcam and had the same issue but I think I should try the INT8 quantized version next time.
Yeah for sure. Should still run way faster doe
Thank you, for sharing, Nicolai.
Thanks for watching Jonathan!
Thanks for putting this together!
Thanks a lot for watching! Means a ton to me
Thank you for your awesome tutorial. Do these models (YOLOv8 & YOLO-NAS) also work on Android? Please share your experience if you have tried it to deploy on Android.
Thank you so much for very good contain.Can we use any other pretrained model instead of coco for YOLO NAS.
Hey Nic. This video is amazing.i have a question for you. Can you help me giving the approach , how to build a helmet detection model for motorbikes and a seat belt detector for cars and other vehicles as a single project? Kindly take the pain to share the right approach and models to use for that.
I did a comparison between yolo and detr in a limited datasets and found yolo is a little better even though both of them results are far from satisfaction. Our training datasets are only around 200.
Cool comparison. I don’t think much has been done to detr recently
I'm actually surprised that YOLO only did a little better. While transformer models tend to outperform pure CNNs, they are also know to be very data hungry as well.
Great lectures.
Thanks a lot man! Appreciate u watching
Love your content but I'm surprised that you didn't use more advanced comparators in the pros and cons like licenses, label format to train on custom data, ... it could have been more informative IMHO
Thanks a lot for watching a the feedback! This was just a quick overview and to more to see how you can run the two models easily. We are definitely going to cover way more of all of that in the future. Stay tuned!
@@NicolaiAI 🤝
Thanks for the video, I have a question. Which one is faster, yolov8 or yolo-NAS? Have you had a chance to test this?
Yolov8 for sure!
How to train this on 8 GPUs on DGX A100?
i tried yolov8 for sometime, and when i want to try tuning hyperparameters using raytune, it shows an error, even though i followed the steps provided by Ultralytics, can you make a video about tuning yolov8 hyperparameters using raytune?
Personally, I would detect you in an instant if I saw you outside :D
1ms, piew!
[That Yolo Guy]
Hahah! Come say hi if you detect me one day
hello, really nice tutorial!
I created a custom yolo detection with yolov8m as a base, but when I run it, as soon as the speed of the objects increases, the algorithm loses track of me and starts to "jerk" the video output. Does anyone have an idea how to fix this?
(yolov8m algorithm trained on 300 custom images)
Thanks so much to anyone who can help me!!
Hi Thanks a lot man! Are u using a tracker?
@@NicolaiAI yes i think, i use model.track(..)
Hi, what graphics card are you using?
Rtx 4090
@@NicolaiAI Thank you!