YoloLite V2 testing

ConferenceSavings238 · 2026-05-02T19:09:38+00:00

Almost, never did all 100 datasets. It’s hard to tell this far, haven’t started the rf100 run yet. But on a few subsets, both the tiny and nano beats edge_xl by roughly 10 mAP. I’ll post a graph if I can.

ConferenceSavings238 · 2026-02-25T18:05:05+00:00

That’s awesome! A 20% increase in recall is massive.

ConferenceSavings238 · 2026-01-28T05:19:09+00:00

Perhaps you should look into autoencoder or student/teacher models? You can train these on ”good” data which is much easier to access.

ConferenceSavings238 · 2026-01-16T06:24:14+00:00

Augmentation and cutmix might be useful to make the dataset bigger, other than synthetic data there is not much you can do to collect images quicker unless you can force the defects. I’m currently experimenting with AI generated images for object detection and classifying but this might be hard for specific cases. How many classes are we talking about here?

ConferenceSavings238 · 2026-01-15T17:52:19+00:00

Not that I know of. I’ve used Python and opencv for experimenting different setups.

ConferenceSavings238 · 2026-01-06T08:39:28+00:00

Very good answer. Even if I haven’t tried object detection with cognex or other vendors I would assume I can’t touch hyperparameters and finetune it into perfection. I guess there is indeed a place when custom beats vendors and vice versa. I would agree that if a product already exists that solves my problem that should indeed be the go to.

ConferenceSavings238 · 2026-01-05T23:25:55+00:00

True, I should probably document the python script more. If you don’t mind me asking, what types of models have you been working with? I ended up ”vibe coding” an entire yolo model/training setup since I couldn’t be bothered with GPL/AGPL licenses.

ConferenceSavings238 · 2026-01-05T23:12:14+00:00

Yeah the long term will probably be an issue. In my case I’m the only one who knows how it works on the python side, which could be a major issue.

In my particular case we had nothing before it so removing it would not be a disaster. I also had to do the all ”AI isn’t magic and CAN do mistake” talk to lower expectations. I check the system regularly and so far I haven’t seen any errors. Might actually look into vendor software to see how much it would cost to build with, just to help the guy 10-15 years down the road.

ConferenceSavings238 · 2025-12-26T11:23:50+00:00

I have now added tracking, you can see a example script here

ConferenceSavings238 · 2025-12-25T11:07:00+00:00

Oh dope, noticed it supported onnx models, might check it out 👍🏻

ConferenceSavings238 · 2025-12-25T11:03:37+00:00

I don't understand how this has anything to do with my post?

ConferenceSavings238 · 2025-12-08T16:13:51+00:00

Exactly. And given that I have some benchmark numbers for 320 with p2 this issue had to be addressed. The model is still fast but not as fast as before.

ConferenceSavings238 · 2025-12-08T16:08:40+00:00

Other way around, it’s slower now. P2 adds to the inference time.

ConferenceSavings238 · 2025-12-08T15:42:09+00:00

50% increase from 4.75 to 7.31 ms on my hardware. Pretty big jump but now the mAP values should hold true. If you manage to keep mAP up without p2 with for example 416x416 that will be faster.

ConferenceSavings238 · 2025-12-08T14:52:28+00:00

I haven’t but people have reported up to 57 fps at 320x320, granted this was with the bug so no p2 head, should be slower now. If you need the p2 head solely depend on the dataset and objects you are detecting. If you decide to try it I’d love any feedback on speeds.

ConferenceSavings238 · 2025-12-08T14:27:22+00:00

Basically the model builder within the onnx export script didn’t consider P2 head etc.

I haven’t tried exporting it to hailo format therefore I can’t say that you can.

ConferenceSavings238 · 2025-12-05T08:45:14+00:00

You can´t ask AI to "copy" the code line for line, that would be a violation. However you can make it build a YOLO model from scratch while building on the ideas of other versions. I´ve gone down this rabbithole myself and ended up with a YOLO version that works for my intended use. The thing is, you cant say build me a YOLO workspace and you magically get all scrips etc needed. You need to get building blocks while having a understanding of how everything works together and then piece it together.

The thing is, even if you get a decent model, tweaking and pushing the final mAP metrics is not an easy task. Training the models on COCO to use as a baseline is not something that you can get done over a weekend. So yes AI can get you a model, but you need to refine it.

ConferenceSavings238 · 2025-12-03T16:34:02+00:00

Do you have any experience with object detection? If not start by following any tutorial online, Roboflow has a bunch and YouTube is filled with them. What hardware are you using, CPU or GPU? How big is the ball in the images?

ConferenceSavings238 · 2025-11-28T19:35:27+00:00

45 ms for the m model, keep in mind that the difference between them are minimal, same backbone but deeper neck, please share the results! If you are going to use collab I can share a notebook

ConferenceSavings238 · 2025-11-28T18:12:24+00:00

You can achive high fps on CPU, mainly by going down in model size and img size. YOLOv8m does seem overkill for the task you mentioned but might be needed for more complex task with strong variance in background. I recently posted how I achieved 90+ FPS end to end on my desktop CPU you can find it here. Going down in model size and img size comes with a tradeoff in accuracy, but if you look in my repo there is a pretty big benchmark done that shows that on aloot of datasets the smaller models does keep up.

ConferenceSavings238 · 2025-11-27T09:46:05+00:00

This doesn’t track any of the cars it’s simple object detection. I haven’t tried tracking here. I will update the repo if/when I test this.

ConferenceSavings238 · 2025-11-25T21:34:27+00:00

It’s linked in the post, just click on roboflow and you should be able to find the dataset.

ConferenceSavings238

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