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--- |
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license: apache-2.0 |
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tags: |
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- RyzenAI |
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- object-detection |
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- vision |
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- YOLO |
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- Pytorch |
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datasets: |
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- COCO |
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metrics: |
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- mAP |
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--- |
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# YOLOv8m model trained on COCO for use in comfyUI nodes |
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YOLOv8m is the medium version of YOLOv8 model trained on COCO object detection (118k annotated images) at resolution 640x640. |
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It was released in [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics). |
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We develop a modified version that could be supported by comfyUI nodes as shown in this git repo. |
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For more information please look into the github and wiki for same [https://github.com/jags111/ComfyUI_Jags_VectorMagic](https://github.com/jags111/ComfyUI_Jags_VectorMagic) |
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We have nodes for detection and segmentation seperately. |
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## Model description |
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Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. |
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YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks. |
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## Intended uses & limitations |
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You can use the raw model for object detection. See the [model hub](https://huggingface.co./models?search=amd/yolov8) to look for all available YOLOv8 models. |
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## How to use |
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Please note all modes ending with seg or have seg in their name will do segmentation+masks. |
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All models having a 'ded' in their name will do a detection of the image. |
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we are geenrally focussed in providing 8m bassed models . |
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But one can use 8l, 8n or 8x models as these are trained on different image sizes and different image sets. |
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Generally the coco and openImage sets from ultralytics are taken for our comfyUI testing. |
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More info and general models for other inferences will be updated as they get trained. |
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### Installation |
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Follow instructions provided in the github pages for installation of the nodes and put the models in the required model folder. |
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### Conclusion |
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```bibtex |
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@software{yolov8_ultralytics, |
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author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu}, |
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title = {Ultralytics YOLOv8}, |
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version = {8.0.0}, |
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year = {2023}, |
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url = {https://github.com/ultralytics/ultralytics}, |
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orcid = {0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069}, |
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license = {AGPL-3.0} |
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} |
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``` |
