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?
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.
Hi, you can put checkpoint_path = path of last epoch weight while loading the model. Then run the training code . Check the code that shows how to load model after training in the colab notebook.
Thank you Sir!
Hi, will do that soon 😀
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?
you can use "metric_to_watch": 'mAP@0.50:0.95'
@@coder_zero Yes, I've used it but it gives me an error message saying it couldn't find it.
Hi Sir, good tutorial. May I know how to check mAP@0.50:0.95 and mAR of the model?
sorry for late reply. you can use "metric_to_watch": 'mAP@0.50:0.95'
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.
Hi, you can put checkpoint_path = path of last epoch weight while loading the model. Then run the training code . Check the code that shows how to load model after training in the colab notebook.
@@coder_zero thanks
thanks. 5 epoch enough for training ?
Hi, 5 epochs are not enough for training a good model. Please focus on early stopping rather than number of epochs.
Hi my group member from SIC
hi sir in visualisation what is data.yaml file
Hi, you can remove that data_yaml_path and it will work. I was experimenting something.
hi it is data_yaml_path is the required argument. Can you pls share the training path for the yaml file@@coder_zero