Hi, the first step is to download it from the HUB by clicking on the top right icon and search for 'infer_neural_style_transfer'. It should then appear in your process library on the left side. If it doesn't show up this means the installation has failed, to check this, click on the plugin manager at the bottom of Ikomia Studio. If 'infer_neural_style_transfer' is written in red it means that something went wrong during the installation. You can check the log on the right side to see what went wrong. I hope this helps, don't hesitate to let us know how it goes. edit: Here is also a link on how to use NST using the API: www.ikomia.ai/blog/neural-style-transfer-guide :)

Hi! Are is an example on how to run an object detection model on your camera: www.ikomia.ai/blog/yolov7-real-time-object-detection-with-ikomia-api

Hello ! Unfortunately it is not possible to train YOLOv9 on multiple datasets. You could try merging your datasets by using an annotation tool like CVAT or via scripts (some packages could be useful like COCO-merger).

@@ikomia7669 thank's for your response. You know why I can't use the file "best.pt" with Ultralytics to show the prediction ?

​@@nassimnady3685 Ultralytics may utilize a variant of the model that's fits their API, potentially including alterations to metadata. I can see that there's a discrepancy in the size of the model weights between Ultralytics and the official repository. Without a detailed examination, it's challenging to specify the exact distinctions. The 'pre-trained.pt' file which available at the official repository (github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c.pt) might also not be directly compatible with Ultralytics.

Thanks Deepsim! To get the output values you can print 'yolov9.get_output(1).to_json()'. This will output a dictionary with the detection results. From that you calculate the center point. You can find more information on how to manage the Object Detection tasks following this link ikomia-dev.github.io/python-api-documentation/_autosummary/_autosummaryv/ikomia.dataprocess.pydataprocess.CObjectDetectionIO.html#cobjectdetectionio. If you need more freedom, the infer_yolo_v9 algorithm will be saved under 'user/Ikomia/Plugins/Python/infer_yolo_v9'. From here you have access to the process inference code and you can add prints if you want.

Hi ! Thanks for you comment. I will try to make a video on the subject but in the meantime you can use the notebook I just prepared. It shows how to create a workflow with YOLOv9 for object detection followed by SAM for segmentation. I hope this helps! github.com/Ikomia-dev/notebooks/blob/main/examples/HOWTO_use_YOLOv9_and_SAM_with_Ikomia_API.ipynb

Thanks a lot for your comment !

Hi Ana, thank you for your comment. Everything is ok now, you can try again.

Hi, this algorithm was built with the GPU flag enabled so it's not working for CPU.

Hi, it's not possible for the moment. All plugins run in Ikomia Studio but I have some good news, we are working on your question! We are developing a new Python API to allow the reuse of all algorithms from the marketplace without Ikomia Studio.

@@ikomia7669 thanks for the updates. That will be awesome.. really appreciate your efforts...all the best 👍..