Just when the pair are most comfortable and content, a game of hide and seek goes awry and Milo drifts downriver in a box. Expressing instincts of pure love and selfless affection, Otis sets off in search of his best friend. After providing a midstream defense of Milo from a bear, Otis does not see his friend again for half the film’s running time. Their reunion is short-lived, however, as not even four minutes later, Milo meets Joyce, a lady cat, and falls in love. As fall turns to winter, and Otis feels increasingly alone, we’re reminded, not only of the rapid maturation of cats and dogs, but also of the all-too familiar and mutable nature of friendship itself. Milo and Otis’ adventures seem to take place over the course of a single year, spanning a remarkable variety of landscapes and environments. This is nothing less than an enchanted fantasy world, like J.R.R. It is the world of quest romance following the river away from the safety of the farm, Milo and Otis’s voyages take them through forest and fen, to the desert and the ocean, within sight of the mountain foothills and into the broad plains. YOLOv8 will handle the resizing of both images and adjusting the bounding box coordinates accordingly.Together and by themselves, Milo and Otis encounter beasts large and small, familiar and outlandish. So you can continue training your model using the command you provided. The model will handle this process automatically. Hence, you do not need to change, adjust or resize the annotations or labels when the images are resized during training. Consequently, these labels will remain consistent regardless of image resizing. This means that the labels are in the range of 0 to 1 relative to the image width and height. When it comes to annotations, YOLOv8 uses relative coordinates rather than absolute pixel values for the bounding box positions. In your case, with the original input image of 1920x1080, the images will be resized to an aspect ratio close to 640x360. When providing image size (imgsz=640) for training your model, YOLOv8 takes care of resizing the images to have their longest dimension set to 640 while maintaining the image's original aspect ratio. I guess this can also help with training. It was mentioned to use rect if image is not square. Some tutorials on the internet can be misleading, that this why I want to understand the input for the model. This is my first time using YOLO and I don't have experience with previous versions. For example, if I take model=YOLOv8x-seg can I pass imgsz=3840 ? I understand that it might take longer and consume more memory. The class (power lines) I'm trying to segment is very small in the image, I want to use the original size as much as I can. Q3: If I'm using already pretrained models/weights (in my case YOLOv8x-seg), the table says the size is 640, can I use it for training on custom size. Does it mean we can provide either one size (longest side size of the image) or both sizes as w, h? Can we do imgsz=3840,2160 for example? Q2: For imgsz parameter, documentation says "size of input images as integer or w,h". In this case if I feed images with size of 3840x2160, the model will convert them to 640x320, right? It was also mentioned somewhere that the model will keep the w and h ratio. Q1: When we are providing imgsz=640 does the model resize the original size to 640? (I believe it was mentioned in the repo). I'm working on semantic segmentation problem and have a custom dataset with the image size of 3840x2160. I checked the documentation and the repo but couldn't find related information to this. I have searched the YOLOv8 issues and discussions and found no similar questions.
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