Well, it would be nice if you could share :

  • the list of problematic yolov10 element-wise operations that are not supported by RVC2
  • the list of all available ops that the RVC2 can perform (a synthetic datasheet)

For example, if the Pixel-Adaptive Convolutions (PAC) operator is applicable for RVC2, then element-wise products could be replaced by PAC with some specific setup and so on (not as efficient as the base element-wise product on standard processors but this could compensate in some setups, maybe RVC2).

Alex

    alexandrebenoit

    Hey, we didn't do a deep dive into slow operations to point out where exactly the issue lies. Based on experience, I would say it's slow because of:

    • A lot of splitting, slicing, and concatenations.
    • SiLU - you can see there are a lot of "branch-outs" due to SiLU activation. Comparing this with YoloV6 which uses ReLU and reparametrization trick like in RepVGGs, you can see V8 or similar is slower.
    • MHSA module definitely doesn't help and is likely the "cherry on top".

    You can see the ONNX file we use here for reference. Feel free to compile this to blob and benchmark it. If you want to dive into optimization a bit yourself you can use this as the baseline.

    If you want per-op performance, OpenVINO provides also a benchmark app that can return per-layer latencies. Note that we are more focused on releasing this rather than optimizing, given that the gain for nano version is 1% mAP compared to V6.

      To add, this seems to hold also on other HW (see relevant issue here). While paper optimizes for computational cost and parameter count, those do not always strongly correlate with the throughput and latency - typically, certain well-known operations might have more ops/params, but can execute faster.

      Yes, indeed, this is then mostly related to data exchange bottlenecks and some complex functions such as Silu.

      Then, next steps could be Yolov10 engineering to adapt to hardware or on the research side to look for v11 ;o)

      In this community, who would be interested in a given direction for collab ?

      Alex

      Hi,

      thanks !

      Well, regarding the provided online tools, is there any up to date standardized performance comparison table on a single or multiple Luxonis products ?

      I saw some tables in the doc but it would be great if the update datetime and maybe model version could be provided.

      Alex

        7 days later

        Hi Team,

        Just a quick follow up, what do I need to change here? Last used this for v7.

        Code:
        _URL = "https://tools.luxonis.com" #"http://tools.luxonis.com/upload" _OUTPUT_FILE_NAME = "output.zip" _FRACTIONS = { "none": 0, "read": 0.1, "initialized": 0.3, "onnx": 0.5, "openvino": 0.65, "blob": 0.8, "json": 0.9, "zip": 1 }

        `def convert_yolo(file_path: str, shape: Union[int, Tuple[int, int]] = 416, version: Literal["v10"] = "v10"):
        files = {'file': open(file_path, 'rb')}
        values = {
        'inputshape': shape if isinstance(shape, int) else " ".join(map(str, shape)),
        'version': version,
        'id': uuid4()
        }
        file_name = _OUTPUT_FILE_NAME
        url = f"{_URL}/upload"
        print(url)

        # progress bar
proc = multiprocessing.Process(target=get_progress, args=(str(values["id"]),))
proc.start()

# upload files
session = requests.Session()
with session.post(url, files=files, data=values, stream=True) as r:
    r.raise_for_status()
    proc.terminate()
    print(f"Conversion complete. Downloading...")

    with open(file_name, 'wb') as f:
        for chunk in r.iter_content(chunk_size=8192):
            # If you have chunk encoded response uncomment if
            # and set chunk_size parameter to None.
            # if chunk:
            f.write(chunk)
return file_name`

        Output:
        https://tools.luxonis.com/upload

        Progress

        HTTP error occurred: 520 Server Error: UNKNOWN for url: https://tools.luxonis.com/upload

        Hi @ChrisCoutureDelValle,

        you can use this script:

        import requests
import multiprocessing
from typing import Union, Tuple, Literal
from uuid import uuid4
import argparse


_URL = "https://tools.luxonis.com" #"http://tools.luxonis.com/upload" _OUTPUT_FILE_NAME = "output.zip" _FRACTIONS = { "none": 0, "read": 0.1, "initialized": 0.3, "onnx": 0.5, "openvino": 0.65, "blob": 0.8, "json": 0.9, "zip": 1 }
_OUTPUT_FILE_NAME = "output.zip"


def get_progress(id: str):
    while True:
        try:
            r = requests.get(f"{_URL}/progress/{id}")
            r.raise_for_status()
            data = r.json()
            print(f"Progress: {data['progress']}")
            if data["progress"] == 1:
                break
        except Exception as e:
            print(f"Error: {e}")
            break


def convert_yolo(file_path: str, shape: Union[int, Tuple[int, int]] = 416, version: Literal["v10"] = "v10"):
    files = {'file': open(file_path, 'rb')}
    values = {
        'inputshape': shape if isinstance(shape, int) else " ".join(map(str, shape)),
        'version': version,
        'id': uuid4()
    }
    file_name = _OUTPUT_FILE_NAME
    url = f"{_URL}/upload"
    print(url)

    # progress bar
    proc = multiprocessing.Process(target=get_progress, args=(str(values["id"]),))
    proc.start()

    # upload files
    session = requests.Session()
    with session.post(url, files=files, data=values, stream=True) as r:
        r.raise_for_status()
        proc.terminate()
        print(f"Conversion complete. Downloading...")

        with open(file_name, 'wb') as f:
            for chunk in r.iter_content(chunk_size=8192):
                # If you have chunk encoded response uncomment if
                # and set chunk_size parameter to None.
                # if chunk:
                f.write(chunk)
    return file_name


def main():
    parser = argparse.ArgumentParser(description="Convert YOLO models")
    parser.add_argument("path", type=str, help="Path to the model's weights")
    args = parser.parse_args()
    convert_yolo(args.path)


if __name__ == "__main__":
    main()

        I tested it with yolov10 nano from Ultralytics and it worked. Btw, if you'd be looking for an inspiration how to write a call api to our tools, you can check out this script.

        Best,
        Jan

          JanCuhel

          Hi Jan,

          I used the script that you sent over to try and convert a custom yolov10n model that I trained and got the same error as Chris. Do you know why I could still be getting this error?

          Thanks,

          Arnav

            This seems like that your models are for some reason not compatible with the ones we are testing on. Could you please (both of you @ChrisCoutureDelValle and @arnavaggs) provide us with more information, for example, specify what is the source of the model you've used (e.g. did you downloaded from Ultralytics)? It would help me a lot if you'd share your models with me, so that I could take a closer look. You can either share it with me via Google Drive or you can send it to me via email. My email is jan.cuhel@luxonis.com.

            Best,
            Jan

              JanCuhel I see that for the unit test, the Luxonis team is leveraging these weights. I have been using the weights directly from the yolov10 repo, should the weights from the v10 repo and ultralytics weights not be identical?

              `'yolov10n': 'https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10n.pt',`

              'yolov10s': 'https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10s.pt',

              'yolov10m': 'https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10m.pt',

              'yolov10b': 'https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10b.pt',

              'yolov10l': 'https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10l.pt',

              'yolov10x': 'https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10x.pt',

                JanCuhel Hi Jan, just as a follow up I attempted to upload a custom model trained on the ultralytics weights in the same fashion and ran into the same error.

                  In addition when manually uploading the custom v10 weights from the ultralytics weights and the standard v10 weights and this is the error that occurs, I believe there may possibly be an error in the backend when detecting the custom v10 weights?
                  Automatic version detected: YoloV8 (detection only)