Update README.md

README.md

@@ -1,6 +1,57 @@
 ---
 license: apache-2.0
 tags:
-- code
-- art
----
+- 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](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](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](https://huggingface.co/models?search=amd/yolov8) to look for all available YOLOv8 models.
+
+
+## How to use
+
+
+
+### Installation
+
+Follow instructions provided in the github pages for installation of the nodes and put the models in the required model folder.
+
+### Conclusion
+
+
+
+```bibtex
+@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}
+}
+```