minstai11

I am using virtual environment and VS code tool. I am using simple script import torch from ultralytics import YOLO Load the YOLO model (this can be YOLOv5 or YOLOv8...

Hi, could be that prediction try to keep aspect ration because I feed 320x240 video?

still I get video 1/1 (frame 2686/2686) C:\Git\embedded-ai\output_video_2.mp4: 192x224 1 car, 26.0ms Speed: 0.7ms preprocess, 23.2ms inference, 0.7ms postprocess per image at shape (1, 3, 192, 224) does it mean...

Could it be that in ultralytics HUB preview mode does not work as expected, I see that model.predict method works much better than model preview in ultralytics hub platform. ?

Same package and same model, but I get different result on HUB and model.predict with the same image. 

image size, which I have used results_img = model.predict(source=r'C:\Users\stamin\Desktop\image.jpg', save=True, conf=0.6, imgsz=224) Settings, model parameters are the same. model.predict gives 0.96 confidence, while in preview mode you can see less...

Another issue I have done the same training with Yolo 5 and I got different results with the same data. Different recall graphs and metrics. how could you explane it?...

No, I have done draining on the same Yolo v5 model.

No, it is the same data , I have used the same Ultralytics hub, but in 2 weeks difference in time with the same data and my new trained model...

 same model, same dataset different PC, how it could happen? only one day differ and only different PC, also I set pre trained and hub