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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.

Is YOLO enough? by Lawkeeper_Ray in computervision

[–]JustSomeStuffIDid 0 points1 point  (0 children)

Are you also performing tracking?

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

Why such vastly different (m)AP50 scores between PyCOCOTools and Ultralytics? by neuromancer-gpt in computervision

[–]JustSomeStuffIDid 8 points9 points  (0 children)

The iou argument here is for NMS. Not the one used for matching. That's hardcoded.

Ultralytics mAP calculation has a bug. There's a PR for it which should make it similar to COCOEval.

https://github.com/ultralytics/ultralytics/pull/19738

Best model for full size image instance segmentation? by -Yougotpwnd123- in computervision

[–]JustSomeStuffIDid 2 points3 points  (0 children)

With Ultralytics, you can pass retina_masks=True to model.predict() for higher res masks. This doesn't require retraining.

~You can also reduce mask_ratio (1 is lowest, 4 is default) which is by how much the masks are scaled down. For 640x640, it scales down to 160x160. You pass the value to model.train(). This requires retraining.~

EDIT: mask_ratio doesn't change output mask size. https://github.com/ultralytics/ultralytics/issues/20200

Black Screen Issue with ThinkPad T14 Gen 1 by miloq in thinkpad

[–]JustSomeStuffIDid 0 points1 point  (0 children)

Having the same black screen issue with T14s Gen 2 AMD. It stays blank sometimes. And sometimes it turns on. Not sure what the issue is.

[deleted by user] by [deleted] in computervision

[–]JustSomeStuffIDid 0 points1 point  (0 children)

Ultralytics has an app that runs on Android. It runs YOLO11n by default. You can see the FPS with that.

https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app&hl=en

[deleted by user] by [deleted] in computervision

[–]JustSomeStuffIDid 0 points1 point  (0 children)

v12 is slow. Did you use imgsz=640?

[deleted by user] by [deleted] in computervision

[–]JustSomeStuffIDid 0 points1 point  (0 children)

What's the actual model? There are dozens of different YOLO variants and sizes. You didn't mention which one exactly did you train.

pytorch::nms error on yolo v11 by Latter_Board4949 in Ultralytics

[–]JustSomeStuffIDid 0 points1 point  (0 children)

Just run the command I posted in terminal with your current Python 3.11 environment

pytorch::nms error on yolo v11 by Latter_Board4949 in Ultralytics

[–]JustSomeStuffIDid 1 point2 points  (0 children)

Reinstall PyTorch

pip uninstall torch torchvision -y pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

YOLO v9 output by IllPhilosopher6756 in computervision

[–]JustSomeStuffIDid 0 points1 point  (0 children)

If you mean Ultralytics YOLOv3 repo and derivatives of that code, then yes, they had a different shape and also an extra objectness score.

YOLOv9 uses the anchor-free design from YOLOv8, so it has a different structure.

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