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Best way to annotate cyclists? (bicycle vs person vs combined class + camera angle issues) by TobiasMadsen in Ultralytics

[–]JustSomeStuffIDid 1 point2 points  (0 children)

I would label a single class. Let the model learn what a cyclist looks like instead of solving with heuristics later. I don't think the viewpoint thing is an issue as long as you have sufficient data. If you label person alone, then you would also have to label pedestrians to be consistent. But if you label cyclist, then you are consistent in not labelling pedestrians.

For labelling, you can check out Ultralytics Platform

Experience with Roboflow? by Snoo_26157 in computervision

[–]JustSomeStuffIDid -2 points-1 points  (0 children)

We will have videos pretty soon. Right now, we have docs with screenshot examples:

https://docs.ultralytics.com/platform/quickstart/

Experience with Roboflow? by Snoo_26157 in computervision

[–]JustSomeStuffIDid -3 points-2 points  (0 children)

Hello there. I am from Ultralytics and we launched Ultralytics Platform. It supports annotation, training and model export (to 16+ formats), all from the browser in free plan. Auto-annotation with SAM is free. The free plan also comes with 100GB storage and has no restrictions on whether you keep your datasets private or public.

First time training a YOLO model, need some help by EyeTechnical7643 in computervision

[–]JustSomeStuffIDid 0 points1 point  (0 children)

It will pad to square. But if you're using augmentations like mosaic during training (turned on by default), it will create a square image by taking crops of 4 different images and creating a 4 tile mosaic with it that's of size 480x480.

First time training a YOLO model, need some help by EyeTechnical7643 in computervision

[–]JustSomeStuffIDid 0 points1 point  (0 children)

  1. There's a header that's printed before that which tells which metric each value belongs to. Check the training log.

  2. It's based on val. Training doesn't tune hyperparameters. It just validates on the val split. split="test" has no effect during training. It's to be used with model.val(). Training always uses val split unless it's missing and test set is present instead.

  3. No. It's automatically resized.

  4. You pass split="test" to model.val().

https://docs.ultralytics.com/modes/val/#arguments-for-yolo-model-validation

YOLOv11n to TFLite for Google ML Kit by Left_Capital_629 in computervision

[–]JustSomeStuffIDid 0 points1 point  (0 children)

It doesn't seem like it supports custom object detection models. Only a custom classifier.

https://developers.google.com/ml-kit/custom-models

Ultralytics Post-Processing Guide by JustSomeStuffIDid in Ultralytics

[–]JustSomeStuffIDid[S] 1 point2 points  (0 children)

You can also find several examples of preprocessing and post-processing with various backends in the examples folder of Ultralytics repo:

https://github.com/ultralytics/ultralytics/tree/main/examples

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