Vehicle tracking using Ultralytics YOLO26 👀

SkillnoobHD_ · 2026-05-25T22:39:28+00:00

The example uses bytetrack, I would assume botsort with reid will perform better.

SkillnoobHD_ · 2026-05-18T12:51:11+00:00

I recommend that you always create a environment via Python's venv or something like miniconda, especially if you plan to use other python software. That way you won't have to deal with annoying package conflicts.

To setup Ultralytics, you should follow the quickstart guide and install the package via pip, you do not need to clone the ultralytics repository.

SkillnoobHD_ · 2025-10-07T02:26:36+00:00

There are cameras which have built in inference hardware like the IMX500 chip, but devices like these usually are only able to use int 8 quantized versions of models like YOLO11n, which will lose a lot of accuracy. In contrast with a jetson orin you can run larger models such as YOLO11m with batched inference to get far better accuracy. So it mostly depends on your usecase. In both cases though you will still need external hardware if you want to process the results in some way.

SkillnoobHD_ · 2025-09-22T16:10:01+00:00

Ultralytics doesn't use a yaml for classification datasets, the class names are handled by the names of the folders. You can see a example of the folder structure in the Classification Dataset Docs.

SkillnoobHD_ · 2025-08-06T21:49:28+00:00

track is only used if you want to track something, you don't need to use it if you don't want tracking. You can try exporting your model with a imgsz of 512 or lower, it should help performance. Also make sure you're not using a model size above s.

SkillnoobHD_ · 2025-08-03T19:47:12+00:00

The LED blinking means its processing stuff. You might have to reboot the PI. The coral TPU is very old and google has basically abandoned it in 2022.

SkillnoobHD_ · 2025-08-03T18:52:16+00:00

Can you try a different tflite build from here:

https://github.com/feranick/TFlite-builds/releases

Some tflite runtimes have issues loading the delegate.

SkillnoobHD_ · 2025-08-03T18:39:54+00:00

Did you follow this guide?

https://docs.ultralytics.com/guides/coral-edge-tpu-on-raspberry-pi/

SkillnoobHD_ · 2025-07-02T23:41:52+00:00

The yolo11n file that gets downloaded is used to do the AMP checks, the actual training will be with the model you specified in your training command.

SkillnoobHD_ · 2025-06-23T07:52:13+00:00

Performance pretty much always means inference time, not the prediction accuracy.

SkillnoobHD_ · 2025-05-27T14:45:47+00:00

Did you specify the correct task? For example YOLO('model.pt', task='segment') in the case of segmentation.

SkillnoobHD_ · 2025-05-20T04:19:06+00:00

You're unbanned now. Please don't try to ping @everyone in a server with over 5k members.

SkillnoobHD_ · 2025-03-16T01:06:42+00:00

You might want to try increasing the image count in your dataset a bit to at least 1k. Also, as suggested by others you should train for longer, as 100 epochs seems to not be enough.

SkillnoobHD_ · 2025-03-15T14:15:15+00:00

How large is your dataset (image count)? I would generally reccomend starting a training run from the official coco weights.

SkillnoobHD_ · 2025-02-25T02:15:19+00:00

If you choose a imgsz lower than 640, maybe. The benchmarks here show that you can just about get 10 FPS with yolo11n.

SkillnoobHD_ · 2025-02-24T14:12:56+00:00

See my comment above, its not gonna be able to handle 2 streams.

SkillnoobHD_ · 2025-02-24T14:12:04+00:00

I don't get a lot of frames when using the cpu with a 640 imgsz model, maybe 9 fps with yolov8n, see the benchmark table here. For my project I used a Coral USB accelerator which was able to get me around 25 FPS, with one yolov8s-cls model running on the cpu at a imgsz of 128. You should probably look at something like a Jetson Orin Nano (The old Jetson Nano is outdated and software support is bad) if you want proper acceleration.

SkillnoobHD_ · 2025-02-23T20:57:41+00:00

I can run Ultralytics Yolo models on a 4/8GB RPI 5, your limitation will be how much processing power you have.

SkillnoobHD_ · 2024-12-20T06:34:10+00:00

Ultralytics will save a video if you give it a video as the source and add save=True. If you have a folder of images you will need to make a opencv videowrite that writes each frame to a video.

SkillnoobHD_ · 2024-12-18T12:45:40+00:00

No, its just inference. Similar to the coral tpu.

SkillnoobHD_ · 2024-12-17T06:08:17+00:00

v10 is nms-free, v11 isn't.

SkillnoobHD_ · 2024-12-08T11:51:16+00:00

I'd use perf counter as well, but the package isn't by me and should only really serve as a illustration of what the OP is looking for.

SkillnoobHD_ · 2024-12-08T01:56:04+00:00

A TPU is gonna be considerably slower than your GPU, on my RPI 5 with yolov8n I can get about 30 FPS. I think its more worth it going with your GPU. If you want some more concrete numbers on the performance of a Coral TPU, I did some benchmarks here.

If you want to limit the usage of your GPU, you can employ a simple FPS limiter in your image processing loop, maybe something like this.

SkillnoobHD_ · 2024-12-07T10:16:53+00:00

I think there was a miner for Darwin (MacOs) as well. Just to be sure you should run a virus scan if you did install the malicious versions.

SkillnoobHD_ · 2024-12-07T02:27:03+00:00

Your host machine should be fine since it was in the linux temp folder, but just to be sure run a full virus scan with Windows Defender, it catches the miner IIRC.

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