license: apache-2.0
tags:
- RyzenAI
- object-detection
- vision
- YOLO
- Pytorch
datasets:
- COCO
metrics:
- mAP
YOLOv8m model trained on COCO for use in comfyUI nodes
YOLOv8m is the medium version of YOLOv8 model trained on COCO object detection (118k annotated images) at resolution 640x640. It was released in https://github.com/ultralytics/ultralytics.
We develop a modified version that could be supported by comfyUI nodes as shown in this git repo. For more information please look into the github and wiki for same https://github.com/jags111/ComfyUI_Jags_VectorMagic
We have nodes for detection and segmentation seperately.
Model description
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. 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.
Intended uses & limitations
You can use the raw model for object detection. See the model hub to look for all available YOLOv8 models.
How to use
Please note all modes ending with seg or have seg in their name will do segmentation+masks. All models having a 'ded' in their name will do a detection of the image. we are geenrally focussed in providing 8m bassed models . But one can use 8l, 8n or 8x models as these are trained on different image sizes and different image sets. Generally the coco and openImage sets from ultralytics are taken for our comfyUI testing. More info and general models for other inferences will be updated as they get trained.
Installation
Follow instructions provided in the github pages for installation of the nodes and put the models in the required model folder.
Conclusion
@software{yolov8_ultralytics,
author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
title = {Ultralytics YOLOv8},
version = {8.0.0},
year = {2023},
url = {https://github.com/ultralytics/ultralytics},
orcid = {0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069},
license = {AGPL-3.0}
}