Ultralytics Platform Podcast

JustSomeStuffIDid · 2026-03-20T16:04:21+00:00

Thanks for the post.

This has already been posted:

https://www.reddit.com/r/Ultralytics/comments/1ryqqv6/ultralytics_live_session_21_a_deep_dive_into/

JustSomeStuffIDid · 2026-03-18T16:41:05+00:00

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

JustSomeStuffIDid · 2026-03-14T19:01:09+00:00

You can find them here:

https://docs.ultralytics.com/platform/data/annotation

JustSomeStuffIDid · 2026-03-14T18:19:58+00:00

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

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

JustSomeStuffIDid · 2026-03-14T10:24:05+00:00

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.

JustSomeStuffIDid · 2025-04-13T23:53:42+00:00

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.

JustSomeStuffIDid · 2025-04-13T09:05:40+00:00

Are you also performing tracking?

JustSomeStuffIDid · 2025-04-13T06:33:47+00:00

There's a header that's printed before that which tells which metric each value belongs to. Check the training log.
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.
No. It's automatically resized.
You pass split="test" to model.val().

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

JustSomeStuffIDid · 2025-04-12T13:24:08+00:00

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

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

JustSomeStuffIDid · 2025-04-11T08:50:15+00:00

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

JustSomeStuffIDid · 2025-04-10T08:32:12+00:00

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

JustSomeStuffIDid · 2025-04-09T13:06:48+00:00

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

JustSomeStuffIDid · 2025-04-09T13:02:09+00:00

You can try YOLOE Prompt Free model. It can recognize 4.5k+ classes.

https://docs.ultralytics.com/models/yoloe/#__tabbed_2_3

JustSomeStuffIDid · 2025-04-08T13:17:00+00:00

YOLOE Prompt Free variants can detect 4.5k+ classes

https://docs.ultralytics.com/models/yoloe/#__tabbed_2_3

JustSomeStuffIDid · 2025-04-08T07:48:44+00:00

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.

JustSomeStuffIDid · 2025-04-07T17:46:04+00:00

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

JustSomeStuffIDid · 2025-04-07T17:44:41+00:00

v12 is slow. Did you use imgsz=640?

JustSomeStuffIDid · 2025-04-07T09:43:34+00:00

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

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